{"product_id":"data-mining-algorithms-isbn-9781118332580","title":"Data Mining Algorithms","description":"\u003cp\u003e\u003cb\u003e\u003ci\u003eData Mining Algorithms \u003c\/i\u003e\u003c\/b\u003eis a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.\u003c\/p\u003e  \u003cp\u003eAcknowledgements xix\u003c\/p\u003e \u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003eReferences xxxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Preliminaries 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Tasks 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Inductive learning tasks 5\u003c\/p\u003e \u003cp\u003e1.3 Classification 9\u003c\/p\u003e \u003cp\u003e1.4 Regression 14\u003c\/p\u003e \u003cp\u003e1.5 Clustering 16\u003c\/p\u003e \u003cp\u003e1.6 Practical issues 19\u003c\/p\u003e \u003cp\u003e1.7 Conclusion 20\u003c\/p\u003e \u003cp\u003e1.8 Further readings 21\u003c\/p\u003e \u003cp\u003eReferences 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Basic statistics 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 23\u003c\/p\u003e \u003cp\u003e2.2 Notational conventions 24\u003c\/p\u003e \u003cp\u003e2.3 Basic statistics as modeling 24\u003c\/p\u003e \u003cp\u003e2.4 Distribution description 25\u003c\/p\u003e \u003cp\u003e2.5 Relationship detection 47\u003c\/p\u003e \u003cp\u003e2.6 Visualization 62\u003c\/p\u003e \u003cp\u003e2.7 Conclusion 65\u003c\/p\u003e \u003cp\u003e2.8 Further readings 66\u003c\/p\u003e \u003cp\u003eReferences 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Classification 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Decision trees 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 71\u003c\/p\u003e \u003cp\u003e3.2 Decision tree model 72\u003c\/p\u003e \u003cp\u003e3.3 Growing 76\u003c\/p\u003e \u003cp\u003e3.4 Pruning 90\u003c\/p\u003e \u003cp\u003e3.5 Prediction 103\u003c\/p\u003e \u003cp\u003e3.6 Weighted instances 105\u003c\/p\u003e \u003cp\u003e3.7 Missing value handling 106\u003c\/p\u003e \u003cp\u003e3.8 Conclusion 114\u003c\/p\u003e \u003cp\u003e3.9 Further readings 114\u003c\/p\u003e \u003cp\u003eReferences 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Naïve Bayes classifier 118\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 118\u003c\/p\u003e \u003cp\u003e4.2 Bayes rule 118\u003c\/p\u003e \u003cp\u003e4.3 Classification by Bayesian inference 120\u003c\/p\u003e \u003cp\u003e4.4 Practical issues 125\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 131\u003c\/p\u003e \u003cp\u003e4.6 Further readings 131\u003c\/p\u003e \u003cp\u003eReferences 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Linear classification 134\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 134\u003c\/p\u003e \u003cp\u003e5.2 Linear representation 136\u003c\/p\u003e \u003cp\u003e5.3 Parameter estimation 145\u003c\/p\u003e \u003cp\u003e5.4 Discrete attributes 154\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 155\u003c\/p\u003e \u003cp\u003e5.6 Further readings 156\u003c\/p\u003e \u003cp\u003eReferences 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Misclassification costs 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 159\u003c\/p\u003e \u003cp\u003e6.2 Cost representation 161\u003c\/p\u003e \u003cp\u003e6.3 Incorporating misclassification costs 164\u003c\/p\u003e \u003cp\u003e6.4 Effects of cost incorporation 176\u003c\/p\u003e \u003cp\u003e6.5 Experimental procedure 180\u003c\/p\u003e \u003cp\u003e6.6 Conclusion 184\u003c\/p\u003e \u003cp\u003e6.7 Further readings 185\u003c\/p\u003e \u003cp\u003eReferences 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Classification model evaluation 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 189\u003c\/p\u003e \u003cp\u003e7.2 Performance measures 190\u003c\/p\u003e \u003cp\u003e7.3 Evaluation procedures 213\u003c\/p\u003e \u003cp\u003e7.4 Conclusion 231\u003c\/p\u003e \u003cp\u003e7.5 Further readings 232\u003c\/p\u003e \u003cp\u003eReferences 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Regression 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Linear regression 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 237\u003c\/p\u003e \u003cp\u003e8.2 Linear representation 238\u003c\/p\u003e \u003cp\u003e8.3 Parameter estimation 242\u003c\/p\u003e \u003cp\u003e8.4 Discrete attributes 250\u003c\/p\u003e \u003cp\u003e8.5 Advantages of linear models 251\u003c\/p\u003e \u003cp\u003e8.6 Beyond linearity 252\u003c\/p\u003e \u003cp\u003e8.7 Conclusion 258\u003c\/p\u003e \u003cp\u003e8.8 Further readings 258\u003c\/p\u003e \u003cp\u003eReferences 259\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Regression trees 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 261\u003c\/p\u003e \u003cp\u003e9.2 Regression tree model 262\u003c\/p\u003e \u003cp\u003e9.3 Growing 263\u003c\/p\u003e \u003cp\u003e9.4 Pruning 274\u003c\/p\u003e \u003cp\u003e9.5 Prediction 277\u003c\/p\u003e \u003cp\u003e9.6 Weighted instances 278\u003c\/p\u003e \u003cp\u003e9.7 Missing value handling 279\u003c\/p\u003e \u003cp\u003e9.8 Piecewise linear regression 284\u003c\/p\u003e \u003cp\u003e9.9 Conclusion 292\u003c\/p\u003e \u003cp\u003e9.10 Further readings 292\u003c\/p\u003e \u003cp\u003eReferences 293\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Regression model evaluation 295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 295\u003c\/p\u003e \u003cp\u003e10.2 Performance measures 296\u003c\/p\u003e \u003cp\u003e10.3 Evaluation procedures 303\u003c\/p\u003e \u003cp\u003e10.4 Conclusion 309\u003c\/p\u003e \u003cp\u003e10.5 Further readings 309\u003c\/p\u003e \u003cp\u003eReferences 310\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Clustering 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 (Dis)similarity measures 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 313\u003c\/p\u003e \u003cp\u003e11.2 Measuring dissimilarity and similarity 313\u003c\/p\u003e \u003cp\u003e11.3 Difference-based dissimilarity 314\u003c\/p\u003e \u003cp\u003e11.4 Correlation-based similarity 321\u003c\/p\u003e \u003cp\u003e11.5 Missing attribute values 324\u003c\/p\u003e \u003cp\u003e11.6 Conclusion 325\u003c\/p\u003e \u003cp\u003e11.7 Further readings 325\u003c\/p\u003e \u003cp\u003eReferences 326\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 k-Centers clustering 328\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 328\u003c\/p\u003e \u003cp\u003e12.2 Algorithm scheme 330\u003c\/p\u003e \u003cp\u003e12.3 k-Means 334\u003c\/p\u003e \u003cp\u003e12.4 Beyond means 338\u003c\/p\u003e \u003cp\u003e12.5 Beyond (fixed) k 342\u003c\/p\u003e \u003cp\u003e12.6 Explicit cluster modeling 343\u003c\/p\u003e \u003cp\u003e12.7 Conclusion 345\u003c\/p\u003e \u003cp\u003e12.8 Further readings 345\u003c\/p\u003e \u003cp\u003eReferences 347\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Hierarchical clustering 349\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 349\u003c\/p\u003e \u003cp\u003e13.2 Cluster hierarchies 351\u003c\/p\u003e \u003cp\u003e13.3 Agglomerative clustering 353\u003c\/p\u003e \u003cp\u003e13.4 Divisive clustering 361\u003c\/p\u003e \u003cp\u003e13.5 Hierarchical clustering visualization 364\u003c\/p\u003e \u003cp\u003e13.6 Hierarchical clustering prediction 366\u003c\/p\u003e \u003cp\u003e13.7 Conclusion 369\u003c\/p\u003e \u003cp\u003e13.8 Further readings 370\u003c\/p\u003e \u003cp\u003eReferences 371\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Clustering model evaluation 373\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 373\u003c\/p\u003e \u003cp\u003e14.2 Per-cluster quality measures 376\u003c\/p\u003e \u003cp\u003e14.3 Overall quality measures 385\u003c\/p\u003e \u003cp\u003e14.4 External quality measures 393\u003c\/p\u003e \u003cp\u003e14.5 Using quality measures 397\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 398\u003c\/p\u003e \u003cp\u003e14.7 Further readings 398\u003c\/p\u003e \u003cp\u003eReferences 399\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Getting Better Models 401\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Model ensembles 403\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 403\u003c\/p\u003e \u003cp\u003e15.2 Model committees 404\u003c\/p\u003e \u003cp\u003e15.3 Base models 406\u003c\/p\u003e \u003cp\u003e15.4 Model aggregation 420\u003c\/p\u003e \u003cp\u003e15.5 Specific ensemble modeling algorithms 431\u003c\/p\u003e \u003cp\u003e15.6 Quality of ensemble predictions 448\u003c\/p\u003e \u003cp\u003e15.7 Conclusion 449\u003c\/p\u003e \u003cp\u003e15.8 Further readings 450\u003c\/p\u003e \u003cp\u003eReferences 451\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Kernel methods 454\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 454\u003c\/p\u003e \u003cp\u003e16.2 Support vector machines 457\u003c\/p\u003e \u003cp\u003e16.3 Support vector regression 473\u003c\/p\u003e \u003cp\u003e16.4 Kernel trick 482\u003c\/p\u003e \u003cp\u003e16.5 Kernel functions 484\u003c\/p\u003e \u003cp\u003e16.6 Kernel prediction 487\u003c\/p\u003e \u003cp\u003e16.7 Kernel-based algorithms 489\u003c\/p\u003e \u003cp\u003e16.8 Conclusion 494\u003c\/p\u003e \u003cp\u003e16.9 Further readings 495\u003c\/p\u003e \u003cp\u003eReferences 496\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Attribute transformation 498\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 498\u003c\/p\u003e \u003cp\u003e17.2 Attribute transformation task 499\u003c\/p\u003e \u003cp\u003e17.3 Simple transformations 504\u003c\/p\u003e \u003cp\u003e17.4 Multiclass encoding 510\u003c\/p\u003e \u003cp\u003e17.5 Conclusion 521\u003c\/p\u003e \u003cp\u003e17.6 Further readings 521\u003c\/p\u003e \u003cp\u003eReferences 522\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Discretization 524\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 524\u003c\/p\u003e \u003cp\u003e18.2 Discretization task 525\u003c\/p\u003e \u003cp\u003e18.3 Unsupervised discretization 530\u003c\/p\u003e \u003cp\u003e18.4 Supervised discretization 533\u003c\/p\u003e \u003cp\u003e18.5 Effects of discretization 551\u003c\/p\u003e \u003cp\u003e18.6 Conclusion 553\u003c\/p\u003e \u003cp\u003e18.7 Further readings 553\u003c\/p\u003e \u003cp\u003eReferences 556\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Attribute selection 558\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 558\u003c\/p\u003e \u003cp\u003e19.2 Attribute selection task 559\u003c\/p\u003e \u003cp\u003e19.3 Attribute subset search 562\u003c\/p\u003e \u003cp\u003e19.4 Attribute selection filters 568\u003c\/p\u003e \u003cp\u003e19.5 Attribute selection wrappers 588\u003c\/p\u003e \u003cp\u003e19.6 Effects of attribute selection 593\u003c\/p\u003e \u003cp\u003e19.7 Conclusion 598\u003c\/p\u003e \u003cp\u003e19.8 Further readings 599\u003c\/p\u003e \u003cp\u003eReferences 600\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Case studies 602\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 602\u003c\/p\u003e \u003cp\u003e20.2 Census income 605\u003c\/p\u003e \u003cp\u003e20.3 Communities and crime 631\u003c\/p\u003e \u003cp\u003e20.4 Cover type 640\u003c\/p\u003e \u003cp\u003e20.5 Conclusion 654\u003c\/p\u003e \u003cp\u003e20.6 Further readings 655\u003c\/p\u003e \u003cp\u003eReferences 655\u003c\/p\u003e \u003cp\u003eClosing 657\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Notation 659\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Attribute values 659\u003c\/p\u003e \u003cp\u003eA.2 Data subsets 659\u003c\/p\u003e \u003cp\u003eA.3 Probabilities 660\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB R packages 661\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 CRAN packages 661\u003c\/p\u003e \u003cp\u003eB.2 DMR packages 662\u003c\/p\u003e \u003cp\u003eB.3 Installing packages 663\u003c\/p\u003e \u003cp\u003eReferences 664\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC Datasets 666\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIndex 667\u003c\/p\u003e  \u003cp\u003e\u003cstrong\u003ePawel Cichosz\u003c\/strong\u003e, Department of Electronics and Information Technology, Warsaw University of Technology, Poland.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989024588005,"sku":"NP9781118332580","price":89.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118332580.jpg?v=1761782486","url":"https:\/\/k12savings.com\/products\/data-mining-algorithms-isbn-9781118332580","provider":"K12savings","version":"1.0","type":"link"}