{"id":2453,"date":"2022-12-26T10:25:41","date_gmt":"2022-12-26T09:25:41","guid":{"rendered":"https:\/\/ff.mhrooz.xyz\/?p=2453"},"modified":"2022-12-26T10:32:30","modified_gmt":"2022-12-26T09:32:30","slug":"data_mining_-_knowledge_discovery_in_database_1","status":"publish","type":"post","link":"https:\/\/blog.mhrooz.xyz\/index.php\/2022\/12\/26\/data_mining_-_knowledge_discovery_in_database_1\/","title":{"rendered":"Data Mining &#8211; Knowledge Discovery in Database (1)"},"content":{"rendered":"\n<p>I want to make some notes here to deepen my understanding of KDD&#8230;<\/p>\n\n\n\n<!doctype html>\n<html>\n<head>\n<meta charset='UTF-8'><meta name='viewport' content='width=device-width initial-scale=1'>\n<link href='https:\/\/fonts.loli.net\/css?family=PT+Serif:400,400italic,700,700italic&#038;subset=latin,cyrillic-ext,cyrillic,latin-ext' rel='stylesheet' type='text\/css' \/><style type='text\/css'>html {overflow-x: initial !important;}:root { --bg-color: #ffffff; --text-color: 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{\n\tbackground-color: #e8e7df;\n}\n#recent-file-panel tbody tr:nth-child(2n-1) {\n    background-color: transparent !important;\n}\n.megamenu-menu-panel tbody tr:hover td:nth-child(2) {\n    color: inherit;\n}\n.megamenu-menu-panel .btn {\n\tbackground-color: #D2D1D1;\n}\n.btn-default {\n\tbackground-color: transparent;\n}\n.typora-sourceview-on #toggle-sourceview-btn,\n.ty-show-word-count #footer-word-count {\n\tbackground: #c7c5c5;\n}\n#typora-quick-open {\n    background-color: inherit;\n}\n.md-diagram-panel {\n\tmargin-top: 8px;\n}\n.file-list-item-file-name {\n\tfont-weight: initial;\n}\n.file-list-item-summary {\n\topacity: 1;\n}\n.file-list-item {\n\tcolor: #777;\n}\n.file-list-item.active {\n\tbackground-color: inherit;\n\tcolor: black;\n}\n.ty-side-sort-btn.active {\n\tbackground-color: inherit;\n}\n.file-list-item.active .file-list-item-file-name  {\n\tfont-weight: bold;\n}\n.file-list-item{\n    opacity:1 !important;\n}\n.file-library-node.active>.file-node-background{\n\tbackground-color: rgba(32, 43, 51, 0.63);\n\tbackground-color: var(--active-file-bg-color);\n}\n.file-tree-node.active>.file-node-content{\n\tcolor: white;\n\tcolor: var(--active-file-text-color);\n}\n.md-task-list-item>input {\n\tmargin-left: -1.7em;\n\tmargin-top: calc(1rem - 12px);\n\t-webkit-appearance: button;\n}\ninput {\n\tborder: 1px solid #aaa;\n}\n.megamenu-menu-header #megamenu-menu-header-title,\n.megamenu-menu-header:hover, \n.megamenu-menu-header:focus {\n\tcolor: inherit;\n}\n.dropdown-menu .divider {\n\tborder-color: #e5e5e5;\n\topacity: 1;\n}\n\/* https:\/\/github.com\/typora\/typora-issues\/issues\/2046 *\/\n.os-windows-7 strong,\n.os-windows-7 strong  {\n\tfont-weight: 760;\n}\n.ty-preferences .btn-default {\n\tbackground: transparent;\n}\n.ty-preferences .window-header {\n\tborder-bottom: 1px dashed #202B33;\n\tbox-shadow: none;\n}\n#sidebar-loading-template, #sidebar-loading-template.file-list-item {\n\tcolor: #777;\n}\n.searchpanel-search-option-btn.active {\n\tbackground: #777;\n\tcolor: white;\n}\n.export-detail, .light .export-detail, \n.light .export-item.active, \n.light .export-items-list-control {\n\tbackground: #e0e0e0;\n\tborder-radius: 2px;\n\tfont-weight: 700;\n\tcolor: inherit\n}\nmjx-container[jax=\"SVG\"] {\n  direction: ltr;\n}\nmjx-container[jax=\"SVG\"] > svg {\n  overflow: visible;\n  min-height: 1px;\n  min-width: 1px;\n}\nmjx-container[jax=\"SVG\"] > svg a {\n  fill: blue;\n  stroke: blue;\n}\nmjx-assistive-mml {\n  position: absolute !important;\n  top: 0px;\n  left: 0px;\n  clip: rect(1px, 1px, 1px, 1px);\n  padding: 1px 0px 0px 0px !important;\n  border: 0px !important;\n  display: block !important;\n  width: auto !important;\n  overflow: hidden !important;\n  -webkit-touch-callout: none;\n  -webkit-user-select: none;\n  -khtml-user-select: none;\n  -moz-user-select: none;\n  -ms-user-select: none;\n  user-select: none;\n}\nmjx-assistive-mml[display=\"block\"] {\n  width: 100% !important;\n}\nmjx-container[jax=\"SVG\"][display=\"true\"] {\n  display: block;\n  text-align: center;\n  margin: 1em 0;\n}\nmjx-container[jax=\"SVG\"][display=\"true\"][width=\"full\"] {\n  display: flex;\n}\nmjx-container[jax=\"SVG\"][justify=\"left\"] {\n  text-align: left;\n}\nmjx-container[jax=\"SVG\"][justify=\"right\"] {\n  text-align: right;\n}\ng[data-mml-node=\"merror\"] > g {\n  fill: red;\n  stroke: red;\n}\ng[data-mml-node=\"merror\"] > rect[data-background] {\n  fill: yellow;\n  stroke: none;\n}\ng[data-mml-node=\"mtable\"] > line[data-line], svg[data-table] > g > line[data-line] {\n  stroke-width: 70px;\n  fill: none;\n}\ng[data-mml-node=\"mtable\"] > rect[data-frame], svg[data-table] > g > rect[data-frame] {\n  stroke-width: 70px;\n  fill: none;\n}\ng[data-mml-node=\"mtable\"] > .mjx-dashed, svg[data-table] > g > .mjx-dashed {\n  stroke-dasharray: 140;\n}\ng[data-mml-node=\"mtable\"] > .mjx-dotted, svg[data-table] > g > .mjx-dotted {\n  stroke-linecap: round;\n  stroke-dasharray: 0,140;\n}\ng[data-mml-node=\"mtable\"] > g > svg {\n  overflow: visible;\n}\n[jax=\"SVG\"] mjx-tool {\n  display: inline-block;\n  position: relative;\n  width: 0;\n  height: 0;\n}\n[jax=\"SVG\"] mjx-tool > mjx-tip {\n  position: absolute;\n  top: 0;\n  left: 0;\n}\nmjx-tool > mjx-tip {\n  display: inline-block;\n  padding: .2em;\n  border: 1px solid #888;\n  font-size: 70%;\n  background-color: #F8F8F8;\n  color: black;\n  box-shadow: 2px 2px 5px #AAAAAA;\n}\ng[data-mml-node=\"maction\"][data-toggle] {\n  cursor: pointer;\n}\nmjx-status {\n  display: block;\n  position: fixed;\n  left: 1em;\n  bottom: 1em;\n  min-width: 25%;\n  padding: .2em .4em;\n  border: 1px solid #888;\n  font-size: 90%;\n  background-color: #F8F8F8;\n  color: black;\n}\nforeignObject[data-mjx-xml] {\n  font-family: initial;\n  line-height: normal;\n  overflow: visible;\n}\nmjx-container[jax=\"SVG2\"] path[data-c], mjx-container[jax=\"SVG2\"] use[data-c] {\n  stroke-width: 3;\n}\ng[data-mml-node=\"xypic\"] path {\n  stroke-width: inherit;\n}\n.MathJax g[data-mml-node=\"xypic\"] path {\n  stroke-width: inherit;\n}\nmjx-container[jax=\"SVG\"] path[data-c], mjx-container[jax=\"SVG\"] use[data-c] {\n\t\t\t\t\t\t\tstroke-width: 0;\n\t\t\t\t\t\t} @media print { @page {margin: 0 0 0 0;} body.typora-export {padding-left: 0; padding-right: 0;} #write {padding:0;}}\n<\/style><title>Notes_Chapter_1-2<\/title>\n<\/head>\n<body class='typora-export'><div class='typora-export-content'>\n<div id='write'  class=''><div class='md-toc' mdtype='toc'><p class=\"md-toc-content\" role=\"list\"><span role=\"listitem\" class=\"md-toc-item md-toc-h2\" data-ref=\"n2\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#12-what-is-data-mining\">1.2 What is data mining?<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h2\" data-ref=\"n20\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#13-what-kinds-of-data-can-be-mined\">1.3 What kinds of Data can be mined\uff1f<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n23\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#131-database-data\">1.3.1 Database data<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n34\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#132-data-warehouse\">1.3.2 Data warehouse<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n37\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#133-transactional-data\">1.3.3 Transactional data<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n39\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#134-other-data\">1.3.4 Other data<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h2\" data-ref=\"n41\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#14-what-kind-of-patterns-can-be-mined\">1.4 What kind of Patterns can be mined?<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n48\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#141-classconcept-description-characterization-and-discrimination\">1.4.1 Class\/Concept Description: Characterization and Discrimination<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n71\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#142-mining-frequent-patterns-associations-and-correlations\">1.4.2 Mining Frequent Patterns, Associations, and Correlations<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n74\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#1421-\u4e00\u4e2a\u4f8b\u5b50\">1.4.2.1 \u4e00\u4e2a\u4f8b\u5b50<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n83\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#143-classification-and-regression-for-predictive-analysis\">1.4.3 Classification and Regression for Predictive Analysis<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n99\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#144-cluster-analysis\">1.4.4 Cluster Analysis<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n110\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#145-outlier-analysis\">1.4.5 Outlier Analysis<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n113\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#146-are-all-patterns-interesting\">1.4.6 Are All Patterns Interesting?<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n115\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#objective-measures-of-pattern-interestingness-\u6a21\u5f0f\u5174\u8da3\u5ea6\u7684\u5ba2\u89c2\u5ea6\u91cf\">Objective measures of pattern interestingness \u6a21\u5f0f\u5174\u8da3\u5ea6\u7684\u5ba2\u89c2\u5ea6\u91cf<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n121\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#others\">Others <\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n125\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#subjective-interestingness-measures-\u4e3b\u89c2\u5174\u8da3\u5ea6\u91cf\">Subjective interestingness measures \u4e3b\u89c2\u5174\u8da3\u5ea6\u91cf<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h2\" data-ref=\"n127\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#15-which-technologies-are-used\">1.5 Which Technologies Are Used?<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n128\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#151-statistics\">1.5.1 Statistics<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n130\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#152-machine-learning\">1.5.2 Machine Learning<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h1\" data-ref=\"n138\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#2-getting-to-know-your-data\">2 Getting to Know Your Data<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h2\" data-ref=\"n139\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#21-data-objects-and-attribute-types\">2.1 Data Objects and Attribute Types<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n140\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#211-what-is-an-attribute\">2.1.1 What is an Attribute<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n146\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#212-nominal-attribute--categorical-data\">2.1.2 nominal attribute \/ Categorical Data<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n150\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#213-binary-attribute\">2.1.3 binary attribute<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n153\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#214-ordinal-attribute\">2.1.4 Ordinal Attribute<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n158\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#215-numeric-attirbute\">2.1.5 numeric attirbute<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n161\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#interval-scaled\">interval-scaled<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n165\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#ratio-scaled\">ratio-scaled<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n168\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#216-discrete-versus-continuous-attribution\">2.1.6 Discrete versus continuous attribution<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n172\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#extra-metric-data\">Extra: Metric Data<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n175\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#extra-sequence--vector\">Extra: Sequence &amp; Vector<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n178\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#extra-set\">Extra: Set<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n183\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#extra-other-complex-data\">Extra: Other Complex Data<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n188\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#similarity-models-approaches\">Similarity models: Approaches<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h2\" data-ref=\"n218\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#23-data-visualization\">2.3 Data Visualization<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n220\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#231--pixel-oriented-visualization-techniques\">2.3.1  Pixel-Oriented Visualization Techniques<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n223\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#\u4f8b\u5b50\">\u4f8b\u5b50<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n229\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#232-geometric-projection-visualization-techniques\">2.3.2 Geometric Projection Visualization Techniques<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n232\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#scatter-plot\">scatter plot<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n237\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#scatter-plot-matrix\">Scatter-plot matrix<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n250\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#polygonal-plots\">Polygonal Plots<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n256\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#parallel-coordinates\">Parallel Coordinates<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n263\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#spiderweb-model\">Spiderweb Model<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n267\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#233-icon-based-visualization-techniques\">2.3.3 Icon-Based Visualization Techniques<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n268\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#chernoff-faces\">Chernoff faces<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n270\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#extra-data-reduction\">Extra: Data Reduction<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n281\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#aggregation\">Aggregation<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n285\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#basic-aggregates\">Basic Aggregates<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n290\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#distributive-aggregate-measures\">Distributive Aggregate Measures<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n294\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#algebraic-aggregate-measures\">Algebraic Aggregate Measures<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n299\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#holistic-aggregate-measure\">Holistic Aggregate Measure<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n305\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#measuring-the-central-tendency\">Measuring the Central Tendency<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n307\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#algebraic-mean---weighted-arithmetic-mean\">Algebraic: Mean &#8211; weighted arithmetic mean<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n310\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#algebraic-mid-range\">Algebraic: Mid-range <\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n314\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#holistic-median\">Holistic: Median<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n317\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#holistic-mode\">Holistic: Mode<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n319\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#measuring-the-dispersion-of-data\">Measuring the Dispersion of Data<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n320\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#algebraic-variance\">Algebraic Variance<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n326\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#boxplot-analysis\">Boxplot Analysis<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n333\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#extra-data-generalization\">Extra: Data Generalization<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n341\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#histograms\">Histograms<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n343\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#equi-width-histograms\">Equi-width Histograms<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n364\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#equi-height-histograms\">Equi-height Histograms<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n373\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#concept-hierarchy\">Concept Hierarchy<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n375\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#extra-summarization-based-aggregation\">Extra: Summarization-based Aggregation<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n376\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#data-generalization\">Data Generalization<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n379\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#olap\">OLAP<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n380\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#roll-up\">Roll-up<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n382\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#drill-down\">Drill-down<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n384\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#slice-and-dice\">Slice and dice<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h5\" data-ref=\"n386\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#pivot\">Pivot<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h3\" data-ref=\"n390\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#extra-attribute-oriented-inductionaoi\">Extra: Attribute-Oriented Induction(AOI)<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n395\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#basic-idea-45\">Basic Idea 4.5<\/a><\/span><span role=\"listitem\" class=\"md-toc-item md-toc-h4\" data-ref=\"n399\"><a class=\"md-toc-inner\" style=\"cursor: pointer;\" href=\"#example\">Example<\/a><\/span><\/p><\/div><h2 id='12-what-is-data-mining'><span>1<\/span><span>.<\/span><span>2 What is data mining?<\/span><\/h2><p><span>Data mining is the process of discovering interesting patterns and knowledge from large amount of data<\/span><br\/><span>\u6570\u636e\u6316\u6398\u662f\u5728\u5927\u91cf\u6570\u636e\u4e2d\u53d1\u73b0\u6709\u8da3\u6a21\u5f0f\u548c\u77e5\u8bc6\u7684\u5904\u7406\u8fc7\u7a0b<\/span><\/p><p><span>\u5305\u62ec\u516d\u4e2a\u6b65\u9aa4<\/span><\/p><ol start='' ><li><span>Data Cleaning (to remove noise and inconsistent data)<\/span><\/li><li><span>Data integration ( where multiple data source will be combined ) P.s. \u73b0\u5728\u4e5f\u6709\u4eba\u628a\u8fd9\u4e24\u4e2a\u6b65\u9aa4\u5408\u4e3a\u4e00\u4e2a\uff0c\u79f0\u4f5c\u662f\u6570\u636e\u9884\u5904\u7406step\uff0c\u8fd9\u4e24\u6b65\u5b8c\u6210\u540e\u7684\u7ed3\u679c\u5c06\u4f1a\u653e\u7f6e\u5230\u6570\u636e\u4ed3\u5e93\u91cc<\/span><\/li><li><span>Data selection ( where data relevant to the analysis task are retrieved from the database )<\/span><\/li><li><span>Data transformation ( where data are transformed and consolidated into forms appropriate for mining by performing summary or aggregation operations ) \u628a\u6570\u636e\u8f6c\u6362\u6210DM\u53ef\u4ee5\u7684\u5f62\u5f0f<\/span><\/li><li><span>Data mining \u4ece\u6570\u636e\u4e2d\u63d0\u53d6\u6709\u7528\u7684pattern<\/span><\/li><li><span>Pattern evaluation \u6a21\u5f0f\u8bc4\u4f30<\/span><\/li><li><span>Knowledge presentation \u53ef\u89c6\u5316\u5c55\u793a<\/span><\/li><\/ol><h2 id='13-what-kinds-of-data-can-be-mined'><span>1<\/span><span>.<\/span><span>3 What kinds of Data can be mined\uff1f<\/span><\/h2><p><span>Data mining can be applied to any kind of data as long as the data are meaningful for the target application \u53ea\u8981\u5bf9\u76ee\u6807\u5e94\u7528\u6709\u7528\uff0c\u5c31\u53ef\u4ee5\u7528\u5230\u4efb\u4f55data\u4e0a<\/span><\/p><p><span>\u53ef\u4ee5\u7528\u6765\u6316\u6398\u7684\u57fa\u7840\u5f62\u5f0f\u6709\u4e09\u79cd Database data, data warehouse data, transactional data<\/span><\/p><h3 id='131-database-data'><span>1<\/span><span>.<\/span><span>3.1 Database data<\/span><\/h3><p><span>\u6570\u636e\u5e93\u7684\u4f5c\u7528<\/span><\/p><ol start='' ><li><span>\u63d0\u4f9b\u5b9a\u4e49\u6570\u636e\u5e93\u7ed3\u6784\u548c\u6570\u636e\u5b58\u50a8\u7684\u673a\u5236<\/span><\/li><li><span>\u7ba1\u7406\u6570\u636e\u5e93\u5e76\u884c\uff0c\u5171\u4eab\uff0c\u5206\u5e03\u7684\u6743\u9650<\/span><\/li><li><span>\u4fdd\u8bc1\u6570\u636e\u7684\u4e00\u81f4\u6027\u4ee5\u53ca\u4fe1\u606f\u5b89\u5168\uff0c\u5c24\u5176\u662f\u7cfb\u7edf\u635f\u574f\u4e86\u6216\u8005\u662f\u672a\u6388\u6743\u7684\u8bbf\u95ee<\/span><\/li><\/ol><p><span>\u5173\u7cfb\u578b\u6570\u636e\u5e93\u5c31\u662f\u8868\u7684\u96c6\u5408=&gt;\u6bcf\u4e2a\u8868\u5305\u542b\u4e86\u4e00\u4e9b\u5c5e\u6027\u5217(attribute column)\uff0c\u5143\u7ec4\u884c(tuple rows).\u6bcf\u4e2a\u5143\u7ec4\u884c\u90fd\u6709\u4e00\u4e2aunique key. \u7ecf\u5e38\u4f7f\u7528ER\u6570\u636e\u5e93(Entity-relationship database)<\/span><\/p><p><span>\u6316\u6398\u6570\u636e\u5e93\u4e3b\u8981\u662f\u4e3a\u4e86searching for trends or patterns.<\/span><\/p><h3 id='132-data-warehouse'><span>1<\/span><span>.<\/span><span>3.2 Data warehouse<\/span><\/h3><p><span>A data warehouse is a repository of information collected from multiple sources, stored under the same schema, and usually residing at a single site.<\/span><br\/><span>\u4ece\u591a\u4e2a\u7684\u4fe1\u606f\u6e90\u6536\u96c6\u7684\u4fe1\u606f\u5b58\u50a8\u5e93\uff0c\u628a\u8fd9\u4e9b\u5b58\u50a8\u5e93\u7684\u4fe1\u606f\u901a\u8fc7\u6570\u636e\u6e05\u7406\uff0c\u53d8\u6362\uff0c\u96c6\u6210\uff0c\u88c5\u5165\u6765\u6784\u9020\u4e00\u4e2a\u6570\u636e\u4ed3\u5e93<\/span><\/p><p><span>A data warehouse is usually modeled by a multidimensional data structure, called a <\/span><strong><span>data cube<\/span><\/strong><span>, in which each dimension corresponds to an attribute or a set of attributes in the schema, and each cell stores the value of some aggregate measure such as count<\/span><br\/><span>\u6570\u636e\u4ed3\u5e93\u7528\u6570\u636e\u7acb\u65b9\u4f53\u8fdb\u884c\u591a\u7ef4\u6570\u636e\u5efa\u6a21\uff0c\u6bcf\u4e2acell\u5b58\u653e\u67d0\u79cdthe value of some aggregate measure\u805a\u96c6\u5ea6\u91cf\u503c\uff0c\u6bd4\u5982count\/sum\uff0c<\/span><\/p><h3 id='133-transactional-data'><span>1<\/span><span>.<\/span><span>3.3 Transactional data<\/span><\/h3><p><span>\u4e8b\u52a1\u6570\u636e\u5c31\u662f\u6bcf\u4e2a\u6570\u636e\u90fd\u4ee3\u8868\u7740\u4e00\u4e2a\u4e8b\u52a1\uff0c\u4e70\u4e1c\u897f\uff0c\u9884\u5b9a\uff0c\u7f51\u9875\u70b9\u51fb\u7b49\u4e8b\u52a1<\/span><\/p><h3 id='134-other-data'><span>1<\/span><span>.<\/span><span>3.4 Other data<\/span><\/h3><p><span>\u5176\u4ed6\u7684\u6570\u636e\uff0c\u6bd4\u5982\u6709\u5c42\u6b21\u7ed3\u6784\u7684\u6811\uff0c\u56fe\u7b49\u7b49<\/span><\/p><h2 id='14-what-kind-of-patterns-can-be-mined'><span>1<\/span><span>.<\/span><span>4 What kind of Patterns can be mined?<\/span><\/h2><p><strong><span>Data mining functionalities \u6570\u636e\u6316\u6398\u65b9\u6cd5\uff08\u529f\u80fd)<\/span><\/strong><\/p><p><span>characterization and discrimination \u7279\u5f81\u5316\u548c\u533a\u5206<\/span><\/p><p><span>mining of frequent patterns and associations correlation \u9891\u7e41\u6a21\u5f0f\uff0c\u5173\u8054\u548c\u76f8\u5173\u6027\u6316\u6398<\/span><\/p><p><span>classification and regression \u5206\u7c7b\u548c\u56de\u5f52<\/span><\/p><p><span>clustering analysis \u805a\u7c7b\u5206\u6790<\/span><\/p><p><span>outlier analysis \u79bb\u7fa4\u70b9\u5206\u6790<\/span><\/p><h3 id='141-classconcept-description-characterization-and-discrimination'><span>1<\/span><span>.<\/span><span>4.1 Class\/Concept Description: Characterization and Discrimination<\/span><\/h3><p><span>\u7528\u9ad8\u5ea6\u6982\u62ec\u7684\u8bed\u8a00\u6765\u63cf\u8ff0\u6bcf\u4e2a\u7c7b\u548c\u6982\u5ff5(class\/concept). \u63cf\u8ff0\u53ef\u4ee5\u901a\u8fc7Characterization(\u6570\u636e\u7279\u5f81\u5316\uff0c\u6c47\u603b\u76ee\u6807\u7c7b\u7684\u6570\u636e)\u6216\u8005\u662fDiscrimination(\u6570\u636e\u533a\u5206\uff0c\u5c06\u76ee\u6807\u7c7b\u4e0e\u4e00\u4e2a\u6216\u8005\u591a\u4e2a\u5bf9\u6bd4\u7c7b\u8fdb\u884c\u6bd4\u8f83)<\/span><\/p><p><span>Characterization\u7684\u65b9\u6cd5\uff1a<\/span><\/p><ol start='' ><li><span>OLAP rollup\uff0c\u6267\u884c\u7528\u6237\u63a7\u5236\u7684\uff0c\u6cbf\u7740\u6307\u5b9a\u7ef4\u5ea6\u7684\u6570\u636e\u6c47\u603b \u53ef\u4ee5\u67e5\u8be2\u4efb\u610f\u7ef4\u5ea6\u7684\u6570\u636e\uff0c\u4ece\u67d0\u4e2a\u7ef4\u5ea6\u89c2\u5bdf\u7279\u5f81<\/span><\/li><li><span>attribute-oriented induction \u9762\u5411\u5c5e\u6027\u7684\u5f52\u7eb3<\/span><\/li><\/ol><p><span>Characterization\u7684\u7ed3\u679c\u8f93\u51fa\uff1a<\/span><\/p><ol start='' ><li><span>\u4f20\u7edf\u7684\u7edf\u8ba1\u56fe\u8868<\/span><\/li><li><span>generalized relations \/ characteristic rules<\/span><\/li><\/ol><p><span>Data discrimination\u7684\u65b9\u6cd5\uff1a<\/span><\/p><ol start='' ><li><span>\u5c06\u76ee\u6807\u7c7b\u548c\u5bf9\u6bd4\u7c7b\u8fdb\u884c\u6bd4\u8f83<\/span><\/li><\/ol><p><span>Data discrimination \u7ed3\u679c\u8f93\u51fa\uff1a<\/span><\/p><ol start='' ><li><span>\u7279\u5f81\u63cf\u8ff0.\u5305\u62ec\u76ee\u6807\u7c7b\u548c\u5bf9\u6bd4\u7c7b\u7684\u6bd4\u8f83\u5ea6\u91cf\uff0c\u53ef\u4ee5\u533a\u522b\u76ee\u6807\u7c7b\u548c\u5bf9\u6bd4\u7c7b\u3002Discrimination description\u5c06\u88ab\u7528Discriminant rules\u63cf\u8ff0<\/span><\/li><\/ol><p><span>\u672c\u8d28Charaterization\u662f\u603b\u7ed3,discrimination\u662f\u5bf9\u6bd4<\/span><\/p><h3 id='142-mining-frequent-patterns-associations-and-correlations'><span>1<\/span><span>.<\/span><span>4.2 Mining Frequent Patterns, Associations, and Correlations<\/span><\/h3><p><span>Mining frequent Patterns are the patterns that occur frequently in data.<\/span><br\/><span>\u9891\u7e41\u6a21\u5f0f(frequent patterns)\u5c31\u662f\u5728\u6570\u636e\u4e2d\u7ecf\u5e38\u51fa\u73b0\u7684\u6a21\u5f0f<\/span><\/p><p><span>frequent patterns \u5305\u62ec frequent itemsets, frequent subsequences( sequential patterns), frequent substructures. \u4e5f\u5c31\u662f\u7ecf\u5e38\u51fa\u73b0\u7684\u5e8f\u5217\u6216\u8005\u662f\u5176\u4ed6\u6570\u636e\u7ed3\u6784\u3002\u6bd4\u5982\u4e70\u8fc7laptop\u4e4b\u540e\u4e70\u76f8\u673a\u518d\u4e70\u5185\u5b58\u5361\u5728amazon\u4e2d\u7ecf\u5e38\u51fa\u73b0\uff0c\u8fd9\u6837\u5c31\u662f\u4e00\u4e2a\u7ecf\u5e38\u51fa\u73b0\u7684\u5e8f\u5217\u3002\u5c31\u53ef\u4ee5\u88ab\u6316\u6398\u51fa\u6765<\/span><\/p><h4 id='1421-\u4e00\u4e2a\u4f8b\u5b50'><span>1<\/span><span>.<\/span><span>4.2.1 \u4e00\u4e2a\u4f8b\u5b50<\/span><\/h4><pre class=\"md-fences md-end-block ty-contain-cm modeLoaded\" spellcheck=\"false\" lang=\"\"><div class=\"CodeMirror cm-s-inner cm-s-null-scroll CodeMirror-wrap\" lang=\"\"><div style=\"overflow: hidden; position: relative; width: 3px; height: 0px; top: 10px; left: 4px;\"><textarea autocorrect=\"off\" autocapitalize=\"off\" spellcheck=\"false\" tabindex=\"0\" style=\"position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;\"><\/textarea><\/div><div class=\"CodeMirror-scrollbar-filler\" cm-not-content=\"true\"><\/div><div class=\"CodeMirror-gutter-filler\" cm-not-content=\"true\"><\/div><div class=\"CodeMirror-scroll\" tabindex=\"-1\"><div class=\"CodeMirror-sizer\" style=\"margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;\"><div style=\"position: relative; top: 0px;\"><div class=\"CodeMirror-lines\" role=\"presentation\"><div role=\"presentation\" style=\"position: relative; outline: none;\"><div class=\"CodeMirror-measure\"><\/div><div class=\"CodeMirror-measure\"><\/div><div style=\"position: relative; z-index: 1;\"><\/div><div class=\"CodeMirror-code\" role=\"presentation\"><div class=\"CodeMirror-activeline\" style=\"position: relative;\"><div class=\"CodeMirror-activeline-background CodeMirror-linebackground\"><\/div><div class=\"CodeMirror-gutter-background CodeMirror-activeline-gutter\" style=\"left: 0px; width: 0px;\"><\/div><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\">buys(X,\"computer\") =&gt; buys(X,\"software\") <\/span><\/pre><\/div><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\">[support = 1%, confidence = 50%]<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><div style=\"position: absolute; height: 0px; width: 1px; border-bottom-width: 0px; border-bottom-style: solid; border-bottom-color: transparent; top: 48px;\"><\/div><div class=\"CodeMirror-gutters\" style=\"display: none; height: 48px;\"><\/div><\/div><\/div><\/pre><p><span>confidence (\u7f6e\u4fe1\u5ea6) pattern\u76f8\u4fe1\u7a0b\u5ea6<\/span><\/p><p><span>support (\u652f\u6301\u5ea6) \u6709\u591a\u5c11\u6570\u636e\u652f\u6301\u8fd9\u4e2apattern<\/span><\/p><p><span>\u53ea\u6709\u4e00\u4e2apredicate(\u8c13\u8bcd)\u7684\u5173\u8054\u7ed3\u6784\u53eb\u505a single-dimensional association rules(\u5355\u7ef4\u5173\u8054\u89c4\u5219\uff09\uff0c\u53bb\u6389predicate\u53ef\u4ee5\u628a\u4e0a\u9762\u7684\u7ed3\u8bba\u7b80\u5316\u4e3a<\/span><\/p><pre class=\"md-fences md-end-block ty-contain-cm modeLoaded\" spellcheck=\"false\" lang=\"\"><div class=\"CodeMirror cm-s-inner cm-s-null-scroll CodeMirror-wrap\" lang=\"\"><div style=\"overflow: hidden; position: relative; width: 3px; height: 0px; top: 10px; left: 4px;\"><textarea autocorrect=\"off\" autocapitalize=\"off\" spellcheck=\"false\" tabindex=\"0\" style=\"position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;\"><\/textarea><\/div><div class=\"CodeMirror-scrollbar-filler\" cm-not-content=\"true\"><\/div><div class=\"CodeMirror-gutter-filler\" cm-not-content=\"true\"><\/div><div class=\"CodeMirror-scroll\" tabindex=\"-1\"><div class=\"CodeMirror-sizer\" style=\"margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;\"><div style=\"position: relative; top: 0px;\"><div class=\"CodeMirror-lines\" role=\"presentation\"><div role=\"presentation\" style=\"position: relative; outline: none;\"><div class=\"CodeMirror-measure\"><\/div><div class=\"CodeMirror-measure\"><\/div><div style=\"position: relative; z-index: 1;\"><\/div><div class=\"CodeMirror-code\" role=\"presentation\"><div class=\"CodeMirror-activeline\" style=\"position: relative;\"><div class=\"CodeMirror-activeline-background CodeMirror-linebackground\"><\/div><div class=\"CodeMirror-gutter-background CodeMirror-activeline-gutter\" style=\"left: 0px; width: 0px;\"><\/div><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\">computer =&gt; software [ 1%,50% ]<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><\/div><div style=\"position: absolute; height: 0px; width: 1px; border-bottom-width: 0px; border-bottom-style: solid; border-bottom-color: transparent; top: 24px;\"><\/div><div class=\"CodeMirror-gutters\" style=\"display: none; height: 24px;\"><\/div><\/div><\/div><\/pre><pre class=\"md-fences md-end-block ty-contain-cm modeLoaded\" spellcheck=\"false\" lang=\"\"><div class=\"CodeMirror cm-s-inner cm-s-null-scroll CodeMirror-wrap\" lang=\"\"><div style=\"overflow: hidden; position: relative; width: 3px; height: 0px; top: 10px; left: 4px;\"><textarea autocorrect=\"off\" autocapitalize=\"off\" spellcheck=\"false\" tabindex=\"0\" style=\"position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;\"><\/textarea><\/div><div class=\"CodeMirror-scrollbar-filler\" cm-not-content=\"true\"><\/div><div class=\"CodeMirror-gutter-filler\" cm-not-content=\"true\"><\/div><div class=\"CodeMirror-scroll\" tabindex=\"-1\"><div class=\"CodeMirror-sizer\" style=\"margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;\"><div style=\"position: relative; top: 0px;\"><div class=\"CodeMirror-lines\" role=\"presentation\"><div role=\"presentation\" style=\"position: relative; outline: none;\"><div class=\"CodeMirror-measure\"><\/div><div class=\"CodeMirror-measure\"><\/div><div style=\"position: relative; z-index: 1;\"><\/div><div class=\"CodeMirror-code\" role=\"presentation\"><div class=\"CodeMirror-activeline\" style=\"position: relative;\"><div class=\"CodeMirror-activeline-background CodeMirror-linebackground\"><\/div><div class=\"CodeMirror-gutter-background CodeMirror-activeline-gutter\" style=\"left: 0px; width: 0px;\"><\/div><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\">age(X,\"20..29\") AND income(X,\"40K...49K\") =&gt; buys(X,\"laptop\")<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><\/div><div style=\"position: absolute; height: 0px; width: 1px; border-bottom-width: 0px; border-bottom-style: solid; border-bottom-color: transparent; top: 24px;\"><\/div><div class=\"CodeMirror-gutters\" style=\"display: none; height: 24px;\"><\/div><\/div><\/div><\/pre><p><span>\u6d89\u53ca\u5230\u4e0d\u6b62\u4e00\u4e2apredicate\/attribute\u7684pattern,\u6211\u4eec\u5c31\u79f0\u4e4b\u4e3amulti-dimensional association rule(\u591a\u7ef4\u5173\u8054\u89c4\u5219)<\/span><\/p><p><span>\u5982\u679cassociation rules\u4e0d\u80fd\u6ee1\u8db3minimum support threshold(\u6700\u5c0f\u652f\u6301\u5ea6\u9608\u503c)\u4ee5\u53caminimum confidence threshold(\u6700\u5c0f\u7f6e\u4fe1\u5ea6\u9608\u503c),\u6211\u4eec\u5c31\u8981\u628a\u8fd9\u4e2arule\u629b\u5f03\u6389\u3002<\/span><\/p><h3 id='143-classification-and-regression-for-predictive-analysis'><span>1<\/span><span>.<\/span><span>4.3 Classification and Regression for Predictive Analysis<\/span><\/h3><p><span>\u91cd\u70b9\u5728\u4e8eClassification\u662f\u5728\u533a\u522b\u6bcf\u4e00\u4e2aclass. \u4f8b\u5982\u987e\u5ba2\u5bf9\u5546\u54c1\u53cd\u5e94\u7684\u5206\u7c7b\u6807\u53f7\uff0c\u53ef\u4ee5\u63d0\u524d\u9884\u77e5\u4e0b\u4e00\u4e2a\u5546\u54c1\u7684\u7c7blabel\u3002<\/span><\/p><p><span>Regression\u662f\u6839\u636e\u4e4b\u524d\u7684\u6570\u636e\uff0c\u9884\u6d4b\u672a\u6765\u7684\u8fde\u7eed\u503c\u3002<\/span><\/p><p><span>Classification: \u627e\u5230\u4e00\u4e2amodel or function,\u80fd\u591f\u63cf\u8ff0\/\u533a\u5206\u6570\u636e\u7c7b\u6216\u8005\u6982\u5ff5. \u5f97\u5230\u7684\u7ed3\u679c\u662f\u57fa\u4e8e\u5bf9\u8bad\u7ec3\u6570\u636e\u96c6\u7684\u5206\u6790\u3002\u6a21\u578b\u53ef\u4ee5\u7528\u6765\u9884\u6d4b \u7c7blabel\u672a\u77e5 \u7684label\u3002<\/span><\/p><p><span>\u5bfc\u51fa\u6765\u7684\u6a21\u578b\u662f\u600e\u4e48\u8868\u793a\u7684\uff1f\u51b3\u7b56\u6811\u548c\u795e\u7ecf\u7f51\u7edc<\/span><\/p><ul><li><span>\u51b3\u7b56\u6811decision tree \u5c31\u662f \u6d41\u7a0b\u56fe\uff0c\u53ef\u4ee5\u8f6c\u6362\u6210 if then (classification rules)\u89c4\u5219<\/span><\/li><li><span>\u795e\u7ecf\u7f51\u7edcneural network \u5c31\u662f\u4e00\u7ec4\u7c7b\u4f3c\u4e8e\u795e\u7ecf\u5143\u7684\u5904\u7406\u5355\u5143\uff0c\u5355\u5143\u4e4b\u95f4\u52a0\u6743\u8fde\u63a5<\/span><\/li><li><span>\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\uff0c\u652f\u6301\u5411\u91cf\u673a\uff0ck\u6700\u90bb\u8fd1\u5206\u7c7b<\/span><\/li><\/ul><p><span>Classification \u662f\u9884\u6d4blabel\uff0clabel\u5219\u662f\u79bb\u6563\u7684\uff0c\u65e0\u5e8f\u7684\u3002<\/span><\/p><p><span>Regression: \u9884\u6d4b\u7f3a\u5931\u7684\u6216\u8005\u96be\u4ee5\u83b7\u5f97\u7684\u6570\u636e\u503c\u3002Regression\u9884\u6d4b\u7684\u503c\u662f\u8fde\u7eed\u7684<\/span><\/p><p><span>Relevance analysis: \u5728Classification and Regression\u524d\u8fdb\u884c\uff0c\u9009\u51fa\u6765\u76f8\u5173\u7684attribution\uff0c\u5176\u4ed6\u4e0d\u76f8\u5173\u7684\u4e0d\u8003\u8651<\/span><\/p><p><span>\u4e66\u4e2d\u7684\u4f8b1.8\u610f\u601d\u662f\u628a\u5546\u5e97\u4e2d\u7684\u5546\u54c1\u6839\u636e\u63cf\u8ff0\u7279\u6027\u6240\u5bf9\u5e94\u7684\u53cd\u5e94\uff0c\u5bfc\u51fa\u4e00\u4e2a\u6a21\u578b\uff0c\u6765\u9884\u6d4b\u540e\u9762\u65b0\u5546\u54c1\u7684\u53cd\u5e94\u3002\u8fd9\u4e2a\u6a21\u578b\u540c\u65f6\u80fd\u591f\u63d0\u4f9b\u6570\u636e\u96c6\u7684\u63cf\u8ff0<\/span><\/p><h3 id='144-cluster-analysis'><span>1.4.4 Cluster Analysis<\/span><\/h3><p><span>Clustering \u5206\u6790\u7684\u6570\u636e\u7269\u54c1\u4e0d\u9700\u8981\u8003\u8651class-labeled\uff0cClustering\u53ef\u4ee5\u88ab\u7528\u4f5c\u4ea7\u751f\u7c7b\u6807\u53f7<\/span><\/p><p><span>Clustering\u7684\u539f\u5219(principle):<\/span><\/p><ul><li><span>maximizing the intraclass similarity \u6700\u5927\u5316\u7c7b\u5185\u76f8\u4f3c\u6027<\/span><\/li><li><span>Minimizing the interclass similarity \u6700\u5c0f\u5316\u7c7b\u95f4\u76f8\u4f3c\u6027<\/span><\/li><\/ul><p><span>\u6240\u5f62\u6210\u7684cluster\u53ef\u4ee5\u770b\u6210\u4e00\u4e2aclass of objects, \u6211\u4eec\u53ef\u4ee5\u77e5\u9053\u8fd9\u4e2aclass\u7684\u89c4\u5219(rule)<\/span><\/p><p><span>clustering \u53ef\u4ee5\u4fbf\u4e8e taxonomy formation(\u5206\u7c7b\u6cd5\u5f62\u6210)<\/span><\/p><p><span>\u4f8b\u5b50\u662f\u5229\u7528\u5730\u7406\u4f4d\u7f6e\u7ed9\u987e\u5ba2\u5206\u7c7b<\/span><\/p><h3 id='145-outlier-analysis'><span>1.4.5 Outlier Analysis<\/span><\/h3><p><span>\u4e0d\u9075\u4ecegeneral\u89c4\u5219\u7684\u6570\u636e\u79f0\u4f5c\u662foutlier\u3002\u4e00\u822coutlier\u88ab\u89c6\u4f5c\u566a\u58f0\uff0c\u4f46\u662ffraud detection\u53ef\u4ee5\u7528\u8fd9\u4e2a\u5206\u6790<\/span><\/p><p><span>The analysis of outlier data is referred to as outlier analysis(\u79bb\u7fa4\u70b9\u5206\u6790) or anomaly mining(\u5f02\u5e38\u6316\u6398)<\/span><\/p><h3 id='146-are-all-patterns-interesting'><span>1.4.6 Are All Patterns Interesting?<\/span><\/h3><p><span>A pattern is interesting if it is (1) easily understood by humans, (2) valid on new or test data with some degree of certainty, \u6709\u6548\u7684 (3) potentially useful, and \u6f5c\u5728\u6709\u7528\u7684 (4) novel. \u65b0\u9896\u7684 A pattern is also interesting if it validates a hypothesis that the user sought to confirm. An interesting pattern represents <\/span><strong><span>knowledge<\/span><\/strong><span>. \u6709\u8da3\u7684\u6a21\u5f0f\u5c31\u662f\u77e5\u8bc6<\/span><\/p><h4 id='objective-measures-of-pattern-interestingness-\u6a21\u5f0f\u5174\u8da3\u5ea6\u7684\u5ba2\u89c2\u5ea6\u91cf'><span>Objective measures of pattern interestingness \u6a21\u5f0f\u5174\u8da3\u5ea6\u7684\u5ba2\u89c2\u5ea6\u91cf<\/span><\/h4><ul><li><span>support P(X U Y)<\/span><\/li><li><span>confidence P(Y|X)<\/span><\/li><\/ul><h4 id='others'><span>Others <\/span><\/h4><p><span>If-then \u89c4\u5219\u7684 accurancy and coverage \u51c6\u786e\u7387\u548c\u8986\u76d6\u7387<\/span><\/p><p><span>accurancy : \u6309\u7167If-then\u89c4\u5219\u6b63\u786e\u5206\u7c7b\u7684\u6570\u636e\u6240\u5360\u767e\u5206\u6bd4\u3002\u5206\u7c7b\u7684\u6570\u636e\u662f\u6b63\u786e\u7684<\/span><\/p><p><span>coverage : \u89c4\u5219\u53ef\u4ee5\u4f5c\u7528\u7684\u6570\u636e\u7684\u767e\u5206\u6bd4\u3002 \u80fd\u7528\u8fd9\u4e2a\u89c4\u5219\u5206\u7c7b\u7684\u6570\u636e\u7684\u767e\u5206\u6bd4<\/span><\/p><h4 id='subjective-interestingness-measures-\u4e3b\u89c2\u5174\u8da3\u5ea6\u91cf'><span>Subjective interestingness measures \u4e3b\u89c2\u5174\u8da3\u5ea6\u91cf<\/span><\/h4><p><span>\u53d1\u73b0\u7684pattern\u662f\u51fa\u4e4e\u610f\u6599\u7684\u6216\u8005\u662f\u53ef\u4ee5\u4e3a\u7528\u6237\u7684\u884c\u52a8\u63d0\u4f9b\u4fe1\u606f\u7684\uff0c\u6211\u4eec\u628a\u540e\u8005\u6210\u4e3aactionable<\/span><\/p><h2 id='15-which-technologies-are-used'><span>1<\/span><span>.<\/span><span>5 Which Technologies Are Used?<\/span><\/h2><h3 id='151-statistics'><span>1.5.1 Statistics<\/span><\/h3><p><span>Statistics model\u662f\u4e00\u7ec4\u6570\u5b66\u51fd\u6570\uff0c\u51fd\u6570\u4f7f\u7528\u968f\u673a\u53d8\u91cf\u548c\u6982\u7387\u5206\u5e03\u6765\u523b\u753b\u76ee\u6807\u7c7b\u7684\u884c\u4e3a<\/span><\/p><h3 id='152-machine-learning'><span>1.5.2 Machine Learning<\/span><\/h3><ul><li><span>supervised learning = \u5206\u7c7b classification \u5b66\u4e60\u4e2d\u7684\u76d1\u7763\u6765\u81ea\u4e8e\u6570\u636e\u4e2d\u7684label<\/span><\/li><li><span>unsupervied learning \u7c7b\u4f3c\u4e8e\u805a\u7c7b clustering \u5b66\u4e60\u8fc7\u7a0b\u65e0\u76d1\u7763 \u5982\u7ed9\u51fa\u4e00\u4e9b\u624b\u5199\u6570\u5b57\u56fe\u50cf \u673a\u5668\u53ef\u4ee5\u628a\u76f8\u4f3c\u7684\u5206\u7c7b\uff0c\u4f46\u662f\u673a\u5668\u65e0\u6cd5\u8bc6\u522b\u51fa\u8fd9\u4e9b\u56fe\u50cf\u6709\u4ec0\u4e48\u610f\u601d<\/span><\/li><li><span>semi-supervised learning \u4f7f\u7528\u6807\u8bb0\u7684\u548c\u672a\u6807\u8bb0\u7684\u6570\u636e \u6807\u8bb0\u7684\u7528\u6765\u5b66\u4e60 class model, \u672a\u6807\u8bb0\u7684\u7528\u6765\u5b8c\u5584\u8fb9\u754c\u3002<\/span><\/li><\/ul>\n<body>\n<\/html>\n\n\n\n<p>Reference<\/p>\n\n\n\n<p><a href=\"http:\/\/myweb.sabanciuniv.edu\/rdehkharghani\/files\/2016\/02\/The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011.pdfhttp:\/\/myweb.sabanciuniv.edu\/rdehkharghani\/files\/2016\/02\/The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Data Mining. Concepts and Techniques 3rd Edition<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>I want to make some notes here to deepen my understandi<a class=\"more-link\" href=\"https:\/\/blog.mhrooz.xyz\/index.php\/2022\/12\/26\/data_mining_-_knowledge_discovery_in_database_1\/\">\u7ee7\u7eed\u9605\u8bfb<span class=\"screen-reader-text\">&#8220;Data Mining &#8211; Knowledge Discovery in Database (1)&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[61,38],"tags":[60,59,57,56,58],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/blog.mhrooz.xyz\/index.php\/wp-json\/wp\/v2\/posts\/2453"}],"collection":[{"href":"https:\/\/blog.mhrooz.xyz\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.mhrooz.xyz\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.mhrooz.xyz\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.mhrooz.xyz\/index.php\/wp-json\/wp\/v2\/comments?post=2453"}],"version-history":[{"count":3,"href":"https:\/\/blog.mhrooz.xyz\/index.php\/wp-json\/wp\/v2\/posts\/2453\/revisions"}],"predecessor-version":[{"id":2458,"href":"https:\/\/blog.mhrooz.xyz\/index.php\/wp-json\/wp\/v2\/posts\/2453\/revisions\/2458"}],"wp:attachment":[{"href":"https:\/\/blog.mhrooz.xyz\/index.php\/wp-json\/wp\/v2\/media?parent=2453"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.mhrooz.xyz\/index.php\/wp-json\/wp\/v2\/categories?post=2453"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.mhrooz.xyz\/index.php\/wp-json\/wp\/v2\/tags?post=2453"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}