The Data Mining and Knowledge Discovery Handbook 2005

• Oded Maimon, Lior Rokach: Introduction to Knowledge Discovery in Databases. 1-17

Part I: Preprocessing Methods

• Jonathan I. Maletic, Andrian Marcus: Data Cleansing - A Prelude to Knowledge Discovery. 21-36
• Jerzy W. Grzymala-Busse, Witold J. Grzymala-Busse: Handling Missing Attribute Values. 37-57
• Christopher J. C. Burges: Geometric Methods for Feature Extraction and Dimensional Reduction - A Guided Tour. 59-92
• Barak Chizi, Oded Maimon: Dimension Reduction and Feature Selection. 93-111
• Ying Yang, Geoffrey I. Webb, Xindong Wu: Discretization Methods. 113-130
• Irad E. Ben-Gal: Outlier Detection. 131-146

Part II: Supervised Methods

• Oded Maimon, Lior Rokach: Introduction to Supervised Methods. 149-164
• Lior Rokach, Oded Maimon: Decision Trees. 165-192
• Paola Sebastiani, María M. Abad-Grau, Marco Ramoni: Bayesian Networks. 193-230
• Richard A. Berk: Data Mining within a Regression Framework. 231-255
• Armin Shmilovici: Support Vector Machines. 257-276
• Jerzy W. Grzymala-Busse: Rule Induction. 277-294

Part III: Unsupervised Methods

• Alfred Inselberg: Visualization and Data Mining for High Dimensional Datasets. 297-319
• Lior Rokach, Oded Maimon: Clustering Methods. 321-352
• Frank Höppner: Association Rules. 353-376
• Bart Goethals: Frequent Set Mining. 377-397
• Jean-François Boulicaut, Baptiste Jeudy: Constraint-Based Data Mining. 399-416
• Steve Donoho: Link Analysis. 417-432

Part IV: Soft Computing Methods

• Alex Alves Freitas: Evolutionary Algorithms for Data Mining. 435-467
• Shahar Cohen, Oded Maimon: Reinforcement-Learning: An Overview from a Data Mining Perspective. 469-486
• Peter G. Zhang: Neural Networks. 487-516
• Joseph Komem, Moti Schneider: On the Use of Fuzzy Logic in Data Mining. 517-533
• Tsau Young Lin, Churn-Jung Liau: Granular Computing and Rough Sets. 535-561

Part V: Supporting Methods

• Yoav Benjamini, Moshe Leshno: Statistical Methods for Data Mining. 565-587
• Petr Hájek: Logics for Data Mining. 589-602
• Tao Li, Sheng Ma, Mitsunori Ogihara: Wavelet Methods in Data Mining. 603-626
• Daniel Barbará, Ping Chen: Fractal Mining - Self Similarity-based Clustering and its Applications. 627-647
• Sigal Sahar: Interestingness Measures - On Determining What Is Interesting. 649-660
• Maria Halkidi, Michalis Vazirgiannis: Quality Assessment Approaches in Data Mining. 661-696
• Paolo Giudici: Data Mining Model Comparison. 697-714
• Jean-François Boulicaut, Cyrille Masson: Data Mining Query Languages. 715-727

Part VI: Advanced Methods

• Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil: Meta-Learning. 731-748
• Pierre Geurts: Bias vs. Variance Decomposition for Regression and Classification. 749-763
• Gary M. Weiss: Mining with Rare Cases. 765-776
• Haixun Wang, Philip S. Yu, Jiawei Han: Mining Data Streams. 777-792
• Wei Wang, Jiong Yang: Mining High-Dimensional Data. 793-799
• Moty Ben-Dov, Ronen Feldman: Text Mining and Information Extraction. 801-831
• Shashi Shekhar, Pusheng Zhang, Yan Huang: Spatial Data Mining. 833-851
• Nitesh V. Chawla: Data Mining for Imbalanced Datasets: An Overview. 853-867
• Saso Dzeroski: Relational Data Mining. 869-898
• Johannes Fürnkranz: Web Mining. 899-920
• Nora Oikonomakou, Michalis Vazirgiannis: A Review of Web Document Clustering Approaches. 921-943
• Hong Yao, Cory J. Butz, Howard J. Hamilton: Causal Discovery. 945-955
• Lior Rokach: Ensemble Methods for Classifiers. 957-980
• Oded Maimon, Lior Rokach: Decomposition Methodology for Knowledge Discovery and Data Mining. 981-1003
• Vicenç Torra: Information Fusion - Methods and Aggregation Operators. 1005-1016
• Antonio Congiusta, Domenico Talia, Paolo Trunfio: Parallel and Grid-Based Data Mining - Algorithms, Models and Systems for High-Performance KDD. 1017-1041
• Steve Moyle: Collaborative Data Mining. 1043-1056
• Hamid R. Nemati, Christopher D. Barko: Organizational Data Mining. 1057-1067
• Chotirat (Ann) Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn J. Keogh, Michail Vlachos, Gautam Das: Mining Time Series Data. 1069-1103

Part VII: Applications

• Nada Lavrac, Blaz Zupan: Data Mining in Medicine. 1107-1138
• Gautam B. Singh: Learning Information Patterns in Biological Databases. 1139-1158
• Bruno Agard, Andrew Kusiak: Data Mining for Selection of Manufacturing Processes. 1159-1166
• Yoram Reich: Data Mining of Design Products and Processes. 1167-1187
• Gary M. Weiss: Data Mining in Telecommunications. 1189-1201
• Boris Kovalerchuk, Evgenii Vityaev: Data Mining for Financial Applications. 1203-1224
• Anoop Singhal, Sushil Jajodia: Data Mining for Intrusion Detection. 1225-1237
• Mark Last: Data Mining for Software Testing. 1239-1248
• Kurt H. Thearling: Data Mining for CRM. 1249-1259
• Nissan Levin, Jacob Zahavi: Data Mining for Target Marketing. 1261-1301

Part VIII: Software

• Eibe Frank, Mark A. Hall, Geoffrey Holmes, Richard Kirkby, Bernhard Pfahringer: WEKA - A Machine Learning Workbench for Data Mining. 1305-1314
• Pablo Tamayo, Charles Berger, Marcos M. Campos, Joseph Yarmus, Boriana L. Milenova, Ari Mozes, Margaret Taft, Mark F. Hornick, Ramkumar Krishnan, Shiby Thomas, Mark Kelly, Denis Mukhin, Robert Haberstroh, Susie Stephens, Jacek Myczkowsji: Oracle Data Mining - Data Mining in the Database Environment. 1315-1329
• ZhaoHui Tang, Jamie Maclennan, Pyungchul (Peter) Kim: Building Data Mining Solutions with OLE DB for DM and XML for Analysis. 1331-1345
• Jerzy W. Grzymala-Busse: LERS - A Data Mining System. 1347-1351
• Nissan Levin, Jacob Zahavi: GainSmarts Data Mining System for Marketing. 1353-1364
• Abraham Meidan: Wizsoft's WizWhy. 1365-1369
• Joseph Komem, Moti Schneider: DataEngine - Tools for Intelligent Data Analysis and Control. 1371-1377