Verfügbarkeit: Data mining methods

Data mining methods:

Gespeichert in:

Bibliographische Detailangaben
1. Verfasser:	Chattamvelli, Rajan (VerfasserIn)
Format:	Buch
Sprache:	German
Veröffentlicht:	Oxford Alpha Science 2009
Schlagworte:	Datenerhebung Information Retrieval
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	XXIV, 411 S. Ill., graph. Darst.
ISBN:	9781842655238

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Datensatz im Suchindex

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adam_text	Contents Preface ,> л -, i /· « . f и Lisí of Figures xxi List of Tables xxiii 1. BASIC CONCEPTS IN DATA MINING 1 1.1 Introduction ......................................... 1 .1.2 Data Scales ....................................... 1 1.2.1 Data vs Information ..................■ ■ ■ ■........ 2 ;; 1.2.2 Data Types ................................... 3 л 1.3 Data Categories ..................................... 3 1.3.1 Standard Scales of Measurement ....................... 4 .·:; 1.3.2 Nominal Scale ................................. 4 Coding of Nominal Variables ......................... 5 Binary Variable ................................. 6 Coding of Binary Variables .......................... 6 Symmetric vs Asymmetric Binary Variables ................. 7 Ternary Variables ............................... 8 1.3.3 Ordinal Scale ................................... 8 1.3.4 Allowed Operations .............................. 9 1.3.5 Interval Scale ................................... 9 . s Allowed Operations on Interval Data ..................... 10 Interval Data Transformations ........................ 10 1.3.6 Ratio Scale ................................... 10 ą Operations on Ratio Data ........................... 11 Nonstandard Data ............................... 11 Numeric Data Discretisation ......................... 11 Entropy Based Discretisation ......................... 12 1.4 Databases and Data Warehouses ........................... 12 l.V.l Data Warehouses ................................. 12 1.5 Data Mining ...................................... 13 1.6 Supervised and Unsupervised Learning ........................ 13 1.6.1 Steps in Data Mining .............................. 14 1.6.2 , Data Mining Approaches . . ......................... 15 1.6.3 Data Mining Query Language (DMQL) ................... 16 ix x CONTENTS 1.7 Some Applications ....... ι.. .. ;.-■. i. .-.· ..... ...:.;:.,.. ; ... ¡ч Г . .. ;■ 16 1.7.1 Banks .......... .·...>............,.,..,...._____.. . . 16 1.7.2 Communications ..... ........ . . .... . . ,. . . ......... ...... .16 1.7.3 Government ............................ . . . Г.,. .. 17 1.7.4 Hospitals .... ... ... Λ ..ľľ. . I ľ ! . . ľ . ľ ? /? i . . . . . . 17 1.7.5 Insurance? ...... : . . . : ... .:;;.?. . -.. -. ··. -v .·.,-·:·-■;·: .-..; .;;:.?:;. vl7 1.7.6 Sports .... . . ... . .... . . .:. .:■.-. r.. ·;.,.,..:..-..;·. . /. . . .. ■..... 18 1.7.7 Miscellaneous ....... .......... ......... .,.,.,., ... .....,/.....„... f- 18 1.7.8 Summary. . . . . ..,. . . .,. . ... ,;. . . .J(. i . V . . 7 . . /. ľľľľľ 18 1.8 Exercises ........................... . . . ľ . .... .... 18 References ............. .!. .. . . Г. ...ľ. , ľ . . r ...■. 21 2. DATA VISUALISATION TECHNIQUES ....... s, ..... r ... 23 •- 2.1 What is Data Visualisation? . . . . . . . ...... . . .. . . ľ .Т. .. ...... 23 ;ί 2.1.1 Visualisation Catégories . . . . . ......... . . . . . ......... 24 2.1.2 Tables ........: ... . :. : . :...;. /i--; . .■■■. . ;ч y. sr: : 24 5 2.1.3 Graphics ... . . ; . . ....... v. . . , . .... . r . ;. -.- . . .... 25 2.2 One Variable Diagrams ......................·■■:-■:.-■. . -. . -.· 25 ь 2.2.1 «Line Charts .......................... - ■. .-ЇУ . . . У: - 25 2.2.2 Bar Charts ................ . ľ;;v:.::ľ •-.■-Г . . v Г. .^V·. . ... 26 2.2.3 Histogram. ...... л . . ..-.......... ; . . . . . .-. РАЧ . . 27 Desirable Qualities of a Histogram . : . .·... . . :■ . . -. . ... ...·. . 28 ; 2.2.4 Pictogram . . . .·■·.- . . . .· . .·.■ : - . · : ->: . W-:. . УҐХ. . . ľ :. .л ,.· : . ■.-. 29 2.2.5 Time Charts . . .-. . .·<.-■.·,.·.-■.-.-,-. .-..-.. . . .( .[. : . 7 . : . ?■, ,.■. Y. ....,-29 2.2.6 Temporal Histograms . . .·,·.... . ; <vW F.^v : .■ ? л ^:;.,. .. , . . ... ЗО 2.2.7 Spatial Histograms ................ . /■. ; . : .-.- ....... 30 2.2.8 Pareto Diagrams .......... v : ·■:■ J ! ;Г :Л -V У1.1^. ....... 31 2.2.9 Pie-Charts ■■ . ... . Vf. л Г . јЧ .;V.7 : ľ . : :ч: . i-v .^ ........-33 2.2.10 Radar Charts ...... . ..... ........ v :} ^У . y Şj ii ............. .. . ,34 2.2.11 Frequency Polygons and Frequency Curves . . .. v. Ч ;:- ;-. . ... . . . 35 2.2.12 Stem-and-Leaf Plots ............ -:?-~. . .. ... V .;. .Г. ·. . . 35 1 · 2.2.13 Overlay Charts : . .<.- : ·■: ·-. ■■.;_·.- .--.■■r.-,.^;·..-. . .-.:. . v. . . ...: .■. . . ..:36 - 2.3 Multi- variable Diagrams ¡. :■ . .-:.,.. .. i.i .,;..... . .-. . . . .4 . . .; . .... 37 2.3.1 Scatterplot,, , . .,. ... . . . . ,. :Г v^v^S .· . . ; л . . v . v ....... 37 2.3:2 Bubble Chart .................... .-.:: ; : ζ . . ·. ý.- J. . . 38 1 2.3.3 Contour Plots ............. :y:. V. : } -: í · ■■ ■ . . -. ;-. -■- . ....... 38 2.4 Hierarchical Charts .................. ; .^L . : . .... . . . . .... 39 2.4.1 Polar Trees ............ !ľ:ľ . ;:/v .^ : V v : :. V .:;....... 39 2.4.2 Cause-and-Effect Diagrams . . . .:;.:. ν ;t:. v.;. . ...... :-....... 39 2.4:3 Q-Q Plots ............... <-г-.:>~::::~. : ■ . . . ч ·, · · ·.· · · ■ ■ · · 40 2.4.4 Chernoff Plots ................: . :. . -. . ; : . ..._.· -: /. ..... 42 2.4.5 Box and Whisker Plots .................. . V . ..... .■ . 42 2.4.6 Stem Plots ........... .■ г.- . ... .. . .:.. . . ... . . . . ; 42 2.4.7 Miscellaneous Plots and Charts ..... · . . ν .... .· . .....: .... 43 2.4.8 Visualisation in Data Mining· . . . . . .r. : . . ;. . . . .·.......... 44 2.5 Software for Data Visualisation ..-.. .--. ν . ·. : . .-...........:.... 44 2.6 Exercises ........................................ 44 References ..................................... 46 CONTENTS xi 3. PROBABILITY AND STATISTICS 49 3.1 Introduction . . .T. . .................................. 49 3.2 Probability .: ! ľ , ľ. .: ................................ 51 3.2.1 Different Ways to Express Probability .................... 51 3.2.2 A Notation for Probability .......................... 53 3.2.3 ,.;MethooVof Counting ...................... ¿ ....... 54 ^[ Independence of Events ............................ 54 3.2.4 Rules of Probability .............................. 54 ; £ Probability Model ...:. .......................... 56 Entropy* vs Probability ............................. 56 3.3 Venn Diagrams ................■..................... 57 3.3.1 De Morgan s Laws ............................... 57 3.4 Bayes Theorem ........ о . -............................. 58 3.4.1 Bayes Theorem for Conditional Probability ................. 59 , t, - ■■· Odds-Likelihood Ratio Form of Bayes Theorem ............... 60 ■> !.. . Product Rule for Conditional Probability .................. 60 ^.4.2, і Bayes Classification Rule ........................... 60 Rule of Expected Utility . . . . :. ..... -. . . Ґ. ..... ....... 61 3.5; Mathematical Expectation ..................■............. 61 3.6 Statistics: .....................■■ . . .................... 62 3.6.1 Population vs Sample .....;....................... 62 ,, 3.6.2-- ¿Parameter vs Statistic .......-...................... 63 3.7 i Measures of Location .................................. 63 3.7.1 Mean, Median and Mode ........................... 63 Weighted Mean ................................. 65 Advantages of Mean .............................. 65 3.7.2 Median ...................................... 66 Advantages of Median ............................. 67 3.7.3 Mode . ..................................... 67 ; ■ · Advantages of Mode .............................. 67 3.7.4 Geometric Mean ................................ 68 3.7.5 ■ Harmonic Mean .-............................... 69 3.8 Measures of Dispersion ................................. 69 3.8.1 · Range·..-. ■.··.·................................... 69 3.8.2 Inter-Quartile Range ...............-............... 70 ■ -. 3.8.3 Mean Absolute Deviation ........................... 70 • 3.8.4 Variance. .;,.-................................... 70 3.9 Outliers in Data ........................ r ............ 71 ■ 3.9.1 Spatial vs Temporal Outliers ......................... 71 ·_ 3.9.2 Graphical Detection of Outliers . ....................... 72 3.10 Data Transformations . . . . . .Г:. ■. ........................ 72 3.10.1 Change of Origin ................................ 73 3.10.2 Change of Scale. ................................ 73 3.10.3 Change of Origin and Scale .......................... 73 3.10.4 Min-max Transformation ........................... 74 3.10.5 Standard Normalisation .......... :................. 75 3.10.6 Nonlinear Transformations .......................... 75 xii CONTENTS * ; 3.11 Regression Basics.................. . >] ] . ..... V .......... 75 3.11.1 Scatterplots and Regression.................·.-.: . . : ... . . . 76 Advantages of Scatter Plots .......................... 76 3.11.2 Simple Linear Regression .... .... . ..::<. ; .. ;-._... . f.V. . . -78 3.11.3 Ordinary Least Squares (OLS) ., . .... ..:... ....;.;.. .?.{.. . . 78 3.11.4 Weighted Least;Squares (WLS) .·...,·. . . . .:...... .γ Y. . ?. ... 83 3.11.5 Correlation Coefficient .·.·. . ..-. .... . ... . ... .. .... . ... .. .·. 84 3.11.6 From Scatterplot to Correlation . ...... . . .-.. . . ... .. . . . .;.?. ... 84 Interpretation of Correlation Coefficient . . л . . . . . .. ......... 84 3.11.7· Multivariate Data .............;;. ... .............. 85 3.12 Multiple Linear Regression (MLR) ......................:... 85 3.13 Monte Carlo Methods . ... . . . . . .......;..::. ...;. .:... .?.;.. . . 86 Components of Monte Carlo Simulation ........;.;. . ...%. . . . ; 87 3.14 Contingency Tables v : . ? :: .■. .;...·..;.:.:.;.:.:. . . ·■:;; . . . . .-.-, : . . 87 v 3.15 Exercises ■■ . - ::: ■ . : . Γ.Γ.* . . . ... Y.- . . :.;;; : Y: .;. .:. . Γ ....... 88 References. ..... .-„.ΎνΥ. .... Y. .7. . .,. . Y. . ......... 91 4. DATAWAREHOUSING AND OLAP Y; j í îv íľ^a 93 4.1 The Datawarehouse .................-.·.....;;..... .;.-:;. .г.^ 93 4.1.1 Goals of Data Warehousing ................... r.·.:···. .·.:..· -Γ· 95 4.1.2 Advantages of Data Warehousing .....·.;..-.. ..,·_..;... . ι.χ.,τ· · · 96 4.1.3 Datawarehouses vs Databases ........-.·· ■■í·.· ·:■:.·;:·■· ľ· -ć-r-C- · · 98 4.1.4 Operational Data Stores (ODS) ......... ._■-. ..,.;..;./.-...* .;. .-,.;Y98 4.1.5 Metadata Catalogs .......... .-.y.-.:.-....—. ... . .;. ·:> . · 99 4.1.6 The Datawarehousing Team ...........-. : .............. 99 4.1.7 Datawarehouse Architecture .......... .y; ..,..-...-..........100 4.1.8 Building a Datawarehouse ................ ..t.,.,. . . .j. . . 102 4.2 Data Marts ł ....................;.;.>;.:.,,..;.,.-..-,.......104 Advantages of Datamarts ..................■:·:· -с-л- : · . · 105 4.3 ETL . . . .·. . ..·:.; ; .<. . . ■ . . . ·. . ..;.,. ;.:.·. .;.;.:.].. ...... 106 4.3.1 ETL Tools . ....................-.:... .-..;. .;.-·.;;. . . 106 .. ■. 4.4 Data Staging . . . V .................. .:. : ..;.;... ;-. -C- -ľ- · · Ю6 4.4.1 Data Extraction ................... . . r. . ..;..:·. .. :106 4.4.2 Data Cleansing .......................;·■·;· ·;·;· · ■ · Ю7 4.4.3 Replacing Missing Values ...........-.-:. ...;:;...;. .;.;:. . . 108 4.4.4 Data Transformation ........... ... ;... ..-. .... .... . . 108 4.5 Spatial Datawarehouses (SDW) .................... . . . .;. . . . 110 4.6 Distributed Datawarehouses .................... ...-. . . . . . 110 Advantages of DDW;. ;.·■.....,.. г. .Л ■·::■;■ ··:·-·:·;·?· · ■ · · · · 112 4.6.1 Virtual Data Warehouses (VDW) .. ...,..;.;.../. ;. ;.,.-. Г. .... . . 113 4.6.2 Web-based Data Warehouses (WDW) .............. . .... .;. 113 4.7 DW Indexing Y . .;■.;■.·. .; γ .r .............. ;. ..f.:. ..... :/ . ·. . . 114 4.8 Security inDatawarehousing ............... . ... . . ..... ... 114 - 4.9 What is OLAP? u . .Υ. γ .-/. ľ ;-.,-:·,. . .γ . : Y. . . : ..... . . .... ... 115 - 4.10,OLAPysOLTP;. v .·:...;.... ....... .....: ....... ....:... 116 4.10.1 Advantages of OLAP:, ...........,....:. ..... .... . . Y. . . 118 CONTENTS, xiii 4.11; Data Cubes and Cuboids ........................:....... 119 ; , 4.11.1 Dimensional Modeling ................;.·.--. ., . . .. ...... 120 ;. 4.11.2 Concept Hierarchy. .,.._...............,.,. . ... ........... . 120 Fact Table. .............................·.···....... 120 Additive Facts,. ............................. .... 121 , - Dimension Table ................................. 121 • 4:12 OLAP Schemas χ.... ............................... ... 121 ■ - 4.12.1 Star Schema, ............................. ./. . ..... 122 4.12.2 Snowflake Schema ............................... 123 4.12.3 Fact Constellation Schema ..............,.:./.:.;.... .... 124 4.13 OLAP Operations .......... ....................;. . . .; ...... 125 4Л3.1 Roll-Up r. .......... .,. . ...;,.,.... ... .;.;,;.,. .·,.,....... 125 : 4Л3.2 Drill-Down ................... . . -.>■ . . ... ......: 125 .: 4.13.3 Slicing. ·.-.................■,.-,.:. . . . ...... ........ 126 - 413.4 Dicing 1?· .................. : :.:■. ;;...;:.:...... 127 -: 4.13.5 Pivoting .·. ... ;............. -v-. .- y·. .-..:■ :r: ....... 127 ^4.14 OLAP Security ....................! ? . . .·. : . . . . . . .... 128 ,;4.15 OLAP Software . . . . ................. v. . ?. . . í . . Г. . . ... 128 Λ , 4.16 Exercises V , ľ ...... .................. .....;.. ; ..... 128 References ......... . . ......................... 130 5. DECISION TREES 133 , . ,5.1 Graph Theory . ..................................... 133 5.1.1, Drawing Graphs ................................ 135 ~. 5.1.2 Bipartite Graphs .............................. .... 137 - ·. Constructing Bipartite Graphs ......·.....,.............. 139 , í5.2 Trees . ....... ......................................... 139 : : ■ κ, ι DrawingTrees ................................. 140 r.;5.3 Decision Trees .............................. ; ....... 140 .Iі. Chance and Terminal Nodes .............>......;.. ... . . 141 - . /. 5.3.1 Advantages of Decision Trees !................. ....... 141 : ■-■ 5.3.2 Disadvantages of Decision Trees ........·................ 144 :e 5.3.3 Classification . . ľ-V^-V. ·1?:1. ......... . .............. 144 ::;ί 5.3Λ Production Rules ■-■. . :: . ľ : /........ :........ .... . . 146 ; 5.4 Induction Algorithms .................... . . . . . . . ... . . . . 149 .-.;;. 5Λ.1 ID3 Algorithm ..... . ....................: ...... 149 .;; 5.4.2 Building a DT ...................... ľ /. . . . . .... 150 . ľ: 5.4^3 C4.5 Algorithm ........................... ! . ....... 150 5.5; Measures for Node Splitting ........................ ......... 150 - 5.5:1 Gini s Index Measure ............ ..,................. 151 ■■--- 5.5.2 Shannon s Entropy Measure ............................ 151 ■ 5.5.3 Minimum Classification Error Measure ...................... 151 Gain and Impurity w. . r. . . ........................ 152 5.5.4 CHi-squared Automatic Interaction Detector (CHAID) ........... 153 515.5 Classification arid Regression Tree (CART) ................. 154 xiv CONTENTS 5.6 Pruning Decision Trees ................: . .. ...ν. . . ... . . . . 155 5.7 Fiizzy Decision Trees ....................,. . . ......... . . . 156 ■ ■■ Decision Tables V. . . . .-. . ........ . . . . ..· -. ...... . . · · ·. ■ 157 ■ ■ 5.8 Applications. .-._... . .........·......................... .157 Fraud Detection, ■.,.·.·.................. . ■/. - ■ ·. . ... ...... 157 5.9 Software for Decision .Trees .·. . . . :■.·.......... · · · · · ·.......... 159 ^ 5.10 Exercises, ......<.....;... ................ .. . ...... V:;..· . · ■ 159 References. .,.. ...... ... .......... ...... . . . : . . . . . 161 6. ASSOCIATION RULES -■ 165 ^ 6.1 Association Rules . ...-..-.-.·.................. Y . . .... . ... . . 165 6.1.1; Antecedent and Consequent ..............,.„. : ... . . . .·. . . . 167 6.2 Association Rule Measures ......- ·............:;... . . . . ?? . . . .»168 6.2.1 Confidence and Support __.......,..........»· · · · ■ ; ..·. . · .168 6.2.2 Cross-purchase Analysis .. . . . .,. . . . ............ : . . ..· ·· · · 170 6.2.3 Categorical Variables ... . ..........,._. . ... .-. . . . . .; . · . · . 171 ,,. 6.2.4 Sequence-purchase Analysis .................... . .--..-:·. . . 171 -6.3 Association Rule Mining z . . . . . . . ............. r. . ... .... . . 172 6.3.1 Activity Indicators ........................ . У. . . . ľ . 173 6.3.2 Computational Complexity of ARM .......... . . . .. . ... . . 174 6.3.3 Sparse Association Rules ..;.■..·........... . . . V . .......175 (, 6.3.4 Rare Associations ................................ .... . . .177 6.4 Temporal Association Rules ... .Л:. ............ ··.._· . . . · ·,-· · -177 6.4.1 Pareto Analysis .,...................... ... . v. ľ ... . . 178 6.4.2 Paired Comparisons Analysis .......... . . .< . ; . . . . . . . 178 6.4.3 Negative Associations.^ . .·. . . . . . ... . .. . . . . . . . . . . . . . . . . 179 6.4.4, Fuzzy Association Rules :■.■.■. . . . .-... ;.. .--.· ■. ■ ■У . :- . ·■. v . .......179 6.4.5 Plan Mining. .............................. .-ГУ: . v 179 6.5 Generalisations of Association Rules . . ........ .- . ■ .-.ГГ У.- .-.......180 6.6 Extended Association Rules .................... . . I.e. .- . . . . 180 6.6.1 Multi-Level Association Rules (MLAR) .:. . ... .. r . r .ľ. . ......181 і. . 6.6.2 Multi-Dimensional Association Rules (MDAR). :..;...;.. . . .. . . . 181 (ч· 6.6.3 t Constrained Association Rules ..... .........;:. . . . ; ; :. . . . -182 f: 6.6.4- Rule Constraints in Association Rule Mining . . .-.-..j .. . . ..... . ·. 182 r, . 6.6.5, Weighted Association Rule Mining (WARM) .·.-.. .·■......... . . . . 182 6.7 Algorithms for Association Rules .................... . . .-. . . . 183 6.8 Applications ...,..-...:................,.-. .............184 6.8.1- Purchase Domain Application ...........—„·.. ·.· ■......· · · 184 6.8.2 Diagnosis ..,.-;;.;. ...............· · · · ...... · · · · · · · 184 6.8.3 Inventory Arrangement . .{. ............ . ...,. .. . . . . . . ■ ■ 185 6.8.4 j Fraud Detection .. ...;. ..■. ._.]. . ■. ...і . / .f-. ........ 185 6.9 Software for Association Rules, ............: . .-. . ... . . .·. .■. . . 186 6.10 Exercises ,.. .-. . ..;.-.. .............-. ....- ■ . . . . . . . . . . . .... 186 - ■ t References .......-. ............................ 188 . . ■ Î CONTENTS xv 7. CLUSTER ANALYSIS ; г.,, ,.,.„ - :: 191 7.1 Meaning of Clustering- ..,.<.>. . . . ..;:.■..■....;.■ :^. r. ............ 191 7.1.1 Geometric Interpretation ......... :■ . ;.■;::. .... . . ..... . . 192 . ;:<> ¿ Cluster Display ..,./.,................. . ..... . .:.. 192 0 ¿ С· Cluster Formation ......................: : . . . . .:. . .■ . . . 193 7.1.2 Cluster Analysis Step-by-Step .........-■.;: ... ..... . .... . . 194 7.2 Similarity Metrics; -................. ..г.... : .·..-. .... ... . . 194 . 7.2.1 Euclidean Distance Metric (I<2 Metric) ......·..... .... . . . .:. 195 7.2.2 : с Manhattan Metric (Li Metric) ............·.: . . . ...... . ./ 195 . 7.2.3 Minkowski Metric ,. ......................... . . . .!.. . . . . 196 7.2.4 Mahalanobis Distance Metric .......................... 196 7.2.5 Chebychev Metric (Loo Metric)) ........ r. . .;... . . ...■..: . . . 196 • 7.2.6 Other Metrics ■.-.■■.................... . . ;. . . . / ... . · .· 196 7.3 Clustering Algorithms. ...■.._................. . . .:.:.. ... ...... 197 7.3.1 Hierarchical Clustering Algorithms (HC A):.1: .··.; . . . . . ■. ... . . . 197 Agglomerative Algorithm ............. . .... . . . . ; .1 . .198 Divisive Algorithm, j. .;.................... ..... . . 199 7.3.2- Partitioning Algorithms .....................· ....... 200 K-means Clustering Algorithm ........: : .... .......... 201 7.3.3 Density-based Methods ......; ·. ·. ■:■ .- .■:/.. :. Л . :........ 202 7.4 Cluster Validation Techniques (CVT) ............. . -. .... ... . ■. 203 7.5 Applications-. ............................... . .. . . . . 203 1 ^.P-Marketing ........................:.· .......... 203 7.5.2 Insurance .................................... 204 7.5.3 Medical Sciences ................ .■.■.. ..... .... V . . 205 7.5.4 WebMining .................... V.:: .;;■..■ ľ ..;ľl . ■ . 205 7.5.5 Aviation v/. ............:...... .;: :: . . : . .,. ...... 206 7.5.6 Miscellaneous Applications ..; .......... ľ. . . . . ■. .. .:. .206 7.6 Software for Clustering ............. . . . . . ; .* .;. . . . . . . ■ .: . . /. 207 7.7 Exercises -: . -. . . . . ................. . . ■ƒ· ··: ■■ ■ ■ ■ ■ ■ ■ ■ ■ 207 References.- .....> . . : ; 7ι::: . . . . . . . і . .:;. . . .......... 209 8. GENETIC ALGORITHMS - 213 8.1 Introduction:-. ...-.:.-:.■. ............... . . : Λ .1 : ν. : . . . Λ . . 213 ■ 8.1:1 · Searching for Optimálny ............. .: .■.■:. v ;....... 214 8.2 Genetic Learning Model ..................: . ■.;.......... 215 8.2.1 Advantages of Genetic Algorithms ......... . . ..-. ľ . ...... 220 8.2.2 Disadvantages. д.: ................... í/, v . : .;. ; . ľ . . .222 «8.2.3 Steps in GA ..................... .V : ,v ......... 223 8.2.4 A Notation for GA ........,... . ... Л . ; } ... : . . I V. . . 224 8.3 Genetic Operators . .:.:. .::..... . f ...... .Vi v; . V :....... 224 8.3.1 Selection . ....-:-....... . ...,.. .-. . ..-. ;; ■■;■ :....... 225 Roulette Wheel Selection ..... . . . . . . . . . . ■. . .......... 225 . - .: Гл<; · і Advantages of Roulette Wheel Selection . . .... . ... . .._ ....... 226 -> ^Disadvantages of Roulette Wheel, Selection . . V. V. .. . ........ 227 I · Tournament Selection ■„■ ...... .. . . . .. . . . . ../. ..... .>. . 227 5 8.3.2 Simple Crossover (SX) ...... ,;rľ?: f .;. V . . : ... . ........ 227 xvi CONTENTS s:. 8.3.3 Uniform Crossover (UX) ............................ 228 ·> Advantages of Uniform Crossover ........................... 229 ■ι ■ 8.3.4 Multi-Crossover (MX) .....,....... . . . . ... /......... . . . . 229 8.3.5- Mutation .................................. . . ............. 229 ■ .-; · 8.3.6 Inversion ... .....................,........ . . . . ... ....... 230 ; ι 8:3.7 , Advanced Operators .................. . . . . . . . . : . . . . 231 r:·. 8.3.8 .Arithmetic Crossover (AX) ...... ............... . ;. . . . . . ...·■· .../232 ■: ¡8.4 General· Alphabet Set .......·. .............. .... . . . . V. . . 232 i. -8.5 Schema Theorem . ... ............. ...... . . . . ľ . ...... .. .* ... 238 îi -8.5.1 -¿Elitism : . . ľ . . . . ....... . . ____.... . . . . . . . ... . . У. . . . 240 .M 8.5.2 Epistasis. ................; . ....... . .... . ; : . . . : . .;.. 240 ¡8.6 Implementation of GA; ... ;..... . . . . . . . : . . . . :. . . . . . . . . . . . 240 .::: 8.7 Parallel GA (PGA). ..;.. ...... ....... ... ... . .... . .·;./. . . . . . y :. .... 241 8.7.1 Multi-Stage GA ...................... .,·. ....... . . ... . .242 8.7.2 Neuro-Genetic Models (NGM) . . ...... . . . . .·. . . . . .. . . : : . . 242 ■ <·.: 8.8 Genetic Programming .·..;................. .................. 243 ■> 8.9 Applications ............ :-. . . ..... . .......: . . : . . . . . ....... 243 Insurance . .,·. .·... . ....... .... . . . . ......... . . і :/:.... 243 Fraud Detection -............ . . . . . . .Γ. ..... ...... 244 Miscellaneous Applications ... ■.. . . .. . ... .-...... . . ..·■ · · 246 ,8.10 Software for GA „.,.,...,........; .. V. . . . .·. . .v.1 .;:>..:.;; ..; .;. 247 ■8.11 Exercises,:. .,.,.......· ...-,.......................-.-.■.-·.--.·;.;.;. . . 248 ,i ^References .,...;.....,...................-...;.;. . ....;. . . . 249 9. NEURAL NETWORKS ,jV /í 253 (( 9.1 Introduction to Neural Networks ................... .,. . . .·,.;■ · ■ 253 ,..,. Neural Network Inspiration ............. . .,...■_____ . .-.-.· · ■ 255 ,.., 9.1.1 Advantages of Neural Networks . . ... ....-.,. ...____. ... . . · r· · .· · · 256 M 9.2 Components of Neural Networks .............. ........ . . ... .-. 258 9.2.1 Layering Concept,. .................. ............. 258 ,..., Data Transformation and Communication . . ................. 259 Training Phase ... . . . . ..... .... . . . . . . . . . ......... 260 - .-;·_ Training Algorithms . . ... . ... . . . . .... ... г . . ..: . .... . 260 L 9.2.2 Actuation Functions-. . .......... .......... ..-.....-. . . 261 f Sigmoid Function .. . . . ... . ..... .... . . . ~. ... ...... . . . . 262 . ч Running Phase ..... .... . . .......... .·.............. 262 ,,ι^ Pruning Phase . ..... . . . .-. ......... . . . . . . -. . . . . . . . 263 Г 9.3 Network Topologies . ..... ................ . . . . . . : . . . . . . 263 ,,/ FFN vs FBN . : ................. . ... ..- . .■.... . ; . . 264 E/r 9.3.1 Special Types of ANNs . . ............. ... ... . . . . . . . 265 Single Layer Perceptron (SLP) . . ..... . . . ..... . . . . /. . . . 265 / Multi-Layer Perceptron (MLP) . . . ............ .-. . . .■. -. . . 266 (- ;·■; Knowledge-based Networks .............................. 266 Kohonen Networks ..... ............ ...... -.· ....... 266 с- Self Organising Map (SOM) . . ... . . . . . . . . . . . . . . j ...... 267 ρ,/ Fuzzy-Neural Networks (FNN) ..............· ■ ■ -,...... 269 -;; Stochastic Neural Networks ...... .-. . . . . . . . ..... . . .... 269 Radial Basis Function (RBF) Networks ................... 269 CONTENTS xvii Ϋ ^ г w Probabilistic Neural Networks (PNN)............. ·. . . .·■..; .... ; 270 · í Vi Hopfield Networks (ΗΝ) ............:.:;. /ν; :.;■..: .:. ....... 271 Miscellaneous Types . . .............:.·. .;. . ... .... . ι. ... 272 9.3.2 : Neural Networks vs MLR ................... ........... 273 :! У 9.3.3 ; í Back-propagation Learning ......,.:. ... ..:.:.....: . .·.·.;. . . - 273 Ì ■ i C c - T Backpropagation Algorithm (ВРА) .■ .■ . .:.-...; .- .,: . ... .. . . . ..;. . . 274 Ill-Conditioning ....... . . . г.:: .; . ..;:;;.;■; . . ; :■..·. . ..: ..... . . 276 Implementation Issues ..........,.■>> . .-.·: . . . .... . . ·. . . . 276 9.4 Applications. . .Ч-.. . :■ : . ľ .-: . ...................... .ι . ..,276 .-·: Advertising and Media Planning . . . .-....;: . . . . .-. . . . ... : . . . 277 <· Pattern Recognition .............■·■;·.· .· ....... ... ...·. . . 278 í ! < Classification ............, ,................ . . .■;.-...... 279 £ ·» Data Compression Z . ........ . .„Γ. .>.... ...... .·,.·;.·;. . . 280 : --- -Speaker Identification ............................. . > 280 ■ Web Minings .................,·.;.; .-.■..■.;: .;.;. ... ........ 280 - - - BiometricsV-. : .;.· . /......................:...... 281 ■ ·■ Miscellaneous Applications ................. ... ...... 282 9.5 Software for Neural Networks ..........;... · . . . . .. ......... 282 9.6 Exercises Ч ..■/··: r: «;1.-; ......, ...,;.;.;.:. /... . . . : . . .:...... 283 References . .ľ . . . .................-......... .... .ι . 285 10. WEB MINING > 295 10.1 Web Sites -r-. ■/. ..............·-;··;: .- : . . r . . . ; . ..;:.. . . 295 10.1.1 Web Pages- ; . . . ;,. . . : .................,...;....... ... 295 s.; f 10.1.2 Search Engines ..... .·..;. . ..... . . .·,.. ..-. ■..·.. ...-.;....... 297 -: 10.1.3 Indexers........... . . ...........·..;....-............. 298 10.1.4 Information Extraction .............,.,............ . . . 298 10.1.5 Linguistic Search Engines ........... .. . .* .■■ . ¡. . .._____ . ...... 298 10.2 Web Mining ·: !r? r:rť ť: / : ;:! .;■.■. . ........ .;....·.·..·....... 299 10.2.1 Advantages of Web Mining ................ . ..:·....:........ 299 L 10.2.2 implementing Web Mining .........../;./.. .;. .....;. . . . . . . 300 10.3 Web Content Mining (WCM) ...... . . . .,..,. . ..,;....,.. ....·...... 301 10.3.1 Web Usage Mining (WUM) .... . . ,,...,., .; .·.;. .: . . .,: ....... . . 301 10.3.2 Web User Quality Mining .... . . . ..-./...,.;.,... .......... 302 10.4 Web Structure Mining (WSM) ............;.- .·■. ... ..,..;... . . . . 303 10.4.1 Link Mining ................. .,:./. . ..· -. . . . . ... . . . . .. 303 10.4.2 Measures for Web Structure Mining ,.,. ...... .:. .... . . . . ... . . . 304 10.4.3 Link Categorisation ........-.-■.,. . . .- . . ... . . . . .;. ... . . . 305 10.4.4 Link Stepping, ............... ¡. . . ·.-. .,. . . . . .... . . .; 306 10.4.5 Links Analysis . . . : .·.·;.,..;. ..·.;. ............... ...... 307 10.4.6 Web Query Mining (WQM) ................ .: .·........... 307 10.4.7 Query Performance ; Measures ......................... 307 F-score ....................................;.._.; . .ι 308 10.5 Semantic Web Mining ................. .: . ..... . . ... .:.-·.,. . . 309 10.5.1 Metadata Mining .................... : : . . ... . .-. . . . 309 10.5.2 Multilingual Web Mining ....... . . . ../..-!. ........ . . .,. 310 10.5.3 Web Personalisers ................... . . ...............·.;.- . . 310 xviii CONTENTS - 10.6 Texť Minirig Y. : v .. .; ........ . Y . . . . . Y. . . . і . . . .■: : .· .■ . . 311 10.6.1 Text Mining Workflow......·. . . . . . . . . . . . . . . . ..... ... 311 r- 10.6.2 Pre-processing Text.............·.....·..............313 10.6.3 Text Categorisation . .............. .... Y. .......... 313 10.6.4 Mining Texţified Documents . . . :................ .-Λ. . . 314 - > - 10.6.5 Temporal Text Mining (ТТМ) . . . . Y . . . ....... ....... . 314 10.6.6 Distributed Text Mining (DTM)....................... 315 v 10,6.7 Metrics for Text Mining ...·. . . ........ 1 ... Y ......... 316 10.7 Image Mining.........·........ ....■..... .... . . . . . . . . 319 v- 10.7 Л Issues in Image Mining............................. 319 10.7.2 Multimedia Mining ...... . . . . . .- ................... 319 <■■ ■ 10.7.3 Table Mining .::...;,....... ; .,-................ 320 · 10.7.4 Data Stream Mining (DSM) : . . .... . . . . .....YY . . ...... 320 10.8 Applications / . .Λ ... .....:....... . . : Y. . . . . ... ....... 321 ! - Spam-Mail Classification. ......:....:............... 321 • Web-pageClustering .........:..::......... ....... 322 Web Marketing ....:.:.... ... ................... 323 Miscellaneous Applications......... . -. . . . . . ....... . .■-. . 323 ř 10.9 Software:for Web and Text Mining . ·. . . . . . ■: . ; : : : . . -. -. -. . . . . . . . . 323 ^ lO.lOExercises/ : . . . V . ... ... ........ : .....::... . .......; 324 References .................................... 325 C- -h, ·-■■ ■-■ ._ . _j .. 1Γ.1 SUPPORT VECTOR MACHINES :r: 331 v ll.l Introduction. .... . ·...-. ................... . .ľ.Tť.V . -.-. ■ ■ . . 331 - 11.1.1 Structural Risk Minimisation Principle . .... . ■ . ... . :. . . ..·. . . . 332 - - 11.1.2 Linear Separability r. . . . . . ............ ... . . ľ . .-. . . : 333 1 ■ 11.1.3 Solution Techniques .. .V. . .... . . ■ :■ :~:~- .~- S. .^ , . . . ľ . .-л; . . 333 11.1.4 Hyperplane Classifiers . . ; V . ... : . .. . - j .V . . . . . . : ľ .¿. -P . . . 333 ■■^ 11.1.5 svm Classifier . : .:. :. . ......-. -......·. . ., -.:. . . . ; : . . . 334 11.1.6 Overlapping Classes . ........: ľ-. . .-. .-ľ : . : ľ : . . . . : . .335 :) 11.1.7 Simple SVM (SSVM): -.4 -.vr^ľv ľ VT . . . . . . . . ľ . ľ /ΥΥΪ- . . 335 ■ 11.1.8 Lagrangiân Formulation . .... . . . . . . ■ . . í ... . . .·. . ■ . ... . . 338 ;ι 11.1.9 Dual SVM Formulation ...... !:.Г. ■ . Γ . .·.. . ľ . . . . .Ύ-Γ . . 338 :: Properties of Dual SVM . ......../.-.... . ..... . .ΎΥ . . . 339 ίι;11.2 WeightedSVM (W-SVM) -. ........... .[. ; . Y. . . Y; . . . . ľ- .Y . . . 340 ; ^11.3 Multi-class SVM (MC-SVM) .-.,...-.■......,....:.. . . : Γ. . V 340 : ■ 11.3.1, Pair-wise SSVM (One-versus-One [OVO]) Y. Y. . Y. ..... Y Y. . . . 340 ;i? 11.3.2 One-versus-All (OVA) SVM . . ........... ... . ■ . . . . Y. Y. . . 341 11.4 Soft-Margin SVM (SM-SVM) .,-....,,......... . . . . .Y . . . . . . . 342 • 11.4.1 Weighted Soft Margin SVM (WSM-SVM) . . . Y. . Y . . ......... 344 ■ - 11.4.2 I/-ŞVM· . Y, . -. . . ... -/ -. ....... . . ■ . . . . . . . Y. ľ . . . Y Y . . 344 : 11.4.3 Pruning YvY.y Y. . ·:■-. .... . . . . . V. . . . . .Y:,. ........ 345 : 11.5 Kernels Y. . Y. ■ _. _-._-.. . ..:.■..:.....: . -. : : —Y. ;. ........... 345 11.5.1 ^Properties of Kernels . . ..-.*.. . ·. . . . =. . . . .............. 346 ::: 11.5.2 Mercer s Theorem . Y ....:. : ; . ¡. ·. · ·. . . . . . ......... . . 346 — 11.6 Nonlinear SVM (ГЉ -SVM) -. ;. . . ·. Y . ■. ■. . Y .....:...... Y. . Y ... 347 c;i 11.6.1 iOther Kernel Algorithms . ■. . -. ■..·.-,. . . . Y .... Y Y. ...... Y. 349 CONTENTS xix 11.7 Support Vector Regression (SVR)...........................349 11.8 Applications of SVM..................................351 11.8.1 Medical Application.............................. 351 11.8.2 Text Categorisation .............................. 352 11.9 SVM Software...................................... 355 11. lOExercises........................................ 355 References .:.·.·................................357 12. LATENT SEMANTIC INDEXING 361 12.1 Vector Space Models.................................. 361 12.1.1 Term-by-Document Matrix.......................... 362 12.1.2 Textual IR -....- ..:. :■,-.:. ,-.--.-.-;.--.· ......................... 362 12.1.3 Geometrie Interpretation -. vr . . ...................... 363 12.2 Latent Semantic Analysis............................... 365 12.2.1..Steps in LSA................................... 365 12.2.2 Characteristics of LSA ............................. 366 12.2.3 -Advantages of LSA -;: . . . . . . . ..................... 366 . ■ 12.2.4: Disadvantages of LSA: ..- . . -......................... 367 12.3 Singular Value Decomposition . .■:■■.....··..................... 369 ľ-. The SVD Algorithm 7. .: ............................. 370 12.4 LSIQuery^:-;;. -^. <·--.-.:.■;. . : :........................... 370 :, 12.4.1 Query Processing ................................ 370 12.5: Applications of LSI ................................... 373 , ; 12.5.1 .LSI in Information Retrieval ......................... 373 12.6 Software for LSI ·. .,: .:.,.:■.-............................. 375 12.7 Exercises., . .,. .-....■.................................... 376 References.. ................................... 378 Appendix-A: The Backpropagation Algorithm 381 Solution to Selected Exercises 383 Indexé гл - r ^ 399
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spelling	Chattamvelli, Rajan Verfasser aut Data mining methods Rajan Chattamvelli Oxford Alpha Science 2009 XXIV, 411 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Datenerhebung (DE-588)4155272-6 gnd rswk-swf Information Retrieval (DE-588)4072803-1 gnd rswk-swf Datenerhebung (DE-588)4155272-6 s Information Retrieval (DE-588)4072803-1 s DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020152547&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Chattamvelli, Rajan Data mining methods Datenerhebung (DE-588)4155272-6 gnd Information Retrieval (DE-588)4072803-1 gnd
subject_GND	(DE-588)4155272-6 (DE-588)4072803-1
title	Data mining methods
title_auth	Data mining methods
title_exact_search	Data mining methods
title_full	Data mining methods Rajan Chattamvelli
title_fullStr	Data mining methods Rajan Chattamvelli
title_full_unstemmed	Data mining methods Rajan Chattamvelli
title_short	Data mining methods
title_sort	data mining methods
topic	Datenerhebung (DE-588)4155272-6 gnd Information Retrieval (DE-588)4072803-1 gnd
topic_facet	Datenerhebung Information Retrieval
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020152547&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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