Data Mining and Knowledge Discovery Handbook 2010

Part 1 / Preprocessing Methods

• Oded Maimon, Lior Rokach:
  Introduction to Knowledge Discovery and Data Mining. 1-15
  
• Jonathan I. Maletic, Andrian Marcus:
  Data Cleansing: A Prelude to Knowledge Discovery. 19-32
  
• Jerzy W. Grzymala-Busse, Witold J. Grzymala-Busse:
  Handling Missing Attribute Values. 33-51
  
• Christopher J. C. Burges:
  Geometric Methods for Feature Extraction and Dimensional Reduction - A Guided Tour. 53-82
  
• Barak Chizi, Oded Maimon:
  Dimension Reduction and Feature Selection. 83-100
  
• Ying Yang, Geoffrey I. Webb, Xindong Wu:
  Discretization Methods. 101-116
  
• Irad Ben-Gal:
  Outlier Detection. 117-130
  

Part 2 / Supervised Methods

• Lior Rokach, Oded Maimon:
  Supervised Learning. 133-147
  
• Lior Rokach, Oded Maimon:
  Classification Trees. 149-174
  
• Paola Sebastiani, María M. Abad-Grau, Marco Ramoni:
  Bayesian Networks. 175-208
  
• Richard A. Berk:
  Data Mining within a Regression Framework. 209-230
  
• Armin Shmilovici:
  Support Vector Machines. 231-247
  
• Jerzy W. Grzymala-Busse:
  Rule Induction. 249-265
  

Part 3 / Unsupervised Methods

• Lior Rokach:
  A survey of Clustering Algorithms. 269-298
  
• Frank Höppner:
  Association Rules. 299-319
  
• Bart Goethals:
  Frequent Set Mining. 321-338
  
• Jean-François Boulicaut, Baptiste Jeudy:
  Constraint-based Data Mining. 339-354
  
• Steve Donoho:
  Link Analysis. 355-368
  

Part 4 / Soft Computing Methods

• Alex Alves Freitas:
  A Review of Evolutionary Algorithms for Data Mining. 371-400
  
• Oded Maimon, Shahar Cohen:
  A Review of Reinforcement Learning Methods. 401-417
  
• G. Peter Zhang:
  Neural Networks For Data Mining. 419-444
  
• Tsau Young Lin, Churn-Jung Liau:
  Granular Computing and Rough Sets - An Incremental Development. 445-468
  
• Swagatam Das, Ajith Abraham:
  Pattern Clustering Using a Swarm Intelligence Approach. 469-504
  
• Lior Rokach:
  Using Fuzzy Logic in Data Mining. 505-520
  

Part 5 / Supporting Methods

• Yoav Benjamini, Moshe Leshno:
  Statistical Methods for Data Mining. 523-540
  
• Petr Hájek:
  Logics for Data Mining. 541-551
  
• Tao Li, Sheng Ma, Mitsunori Ogihara:
  Wavelet Methods in Data Mining. 553-571
  
• Daniel Barbará, Ping Chen:
  Fractal Mining - Self Similarity-based Clustering and its Applications. 573-589
  
• Noa Ruschin Rimini, Oded Maimon:
  Visual Analysis of Sequences Using Fractal Geometry. 591-601
  
• Sigal Sahar:
  Interestingness Measures - On Determining What Is Interesting. 603-612
  
• Maria Halkidi, Michalis Vazirgiannis:
  Quality Assessment Approaches in Data Mining. 613-639
  
• Paolo Giudici:
  Data Mining Model Comparison. 641-654
  
• Jean-François Boulicaut, Cyrille Masson:
  Data Mining Query Languages. 655-664
  

Part 6 / Advanced Methods

• Grigorios Tsoumakas, Ioannis Katakis, Ioannis P. Vlahavas:
  Mining Multi-label Data. 667-685
  
• Vicenç Torra:
  Privacy in Data Mining. 687-716
  
• Ricardo Vilalta, Christophe G. Giraud-Carrier, Pavel Brazdil:
  Meta-Learning - Concepts and Techniques. 717-731
  
• Pierre Geurts:
  Bias vs Variance Decomposition for Regression and Classification. 733-746
  
• Gary M. Weiss:
  Mining with Rare Cases. 747-757
  
• Mohamed Medhat Gaber, Arkady B. Zaslavsky, Shonali Krishnaswamy:
  Data Stream Mining. 759-787
  
• Haixun Wang, Philip S. Yu, Jiawei Han:
  Mining Concept-Drifting Data Streams. 789-802
  
• Wei Wang, Jiong Yang:
  Mining High-Dimensional Data. 803-808
  
• Moty Ben-Dov, Ronen Feldman:
  Text Mining and Information Extraction. 809-835
  
• Shashi Shekhar, Pusheng Zhang, Yan Huang:
  Spatial Data Mining. 837-854
  
• Slava Kisilevich, Florian Mansmann, Mirco Nanni, Salvatore Rinzivillo:
  Spatio-temporal clustering. 855-874
  
• Nitesh V. Chawla:
  Data Mining for Imbalanced Datasets: An Overview. 875-886
  
• Saso Dzeroski:
  Relational Data Mining. 887-911
  
• Johannes Fürnkranz:
  Web Mining. 913-929
  
• Nora Oikonomakou, Michalis Vazirgiannis:
  A Review of Web Document Clustering Approaches. 931-948
  
• Hong Yao, Cory J. Butz, Howard J. Hamilton:
  Causal Discovery. 949-958
  
• Lior Rokach:
  Ensemble Methods in Supervised Learning. 959-979
  
• Lior Rokach, Oded Maimon:
  Data Mining using Decomposition Methods. 981-998
  
• Vicenç Torra:
  Information Fusion - Methods and Aggregation Operators. 999-1008
  
• Antonio Congiusta, Domenico Talia, Paolo Trunfio:
  Parallel and Grid-Based Data Mining - Algorithms, Models and Systems for High-Performance KDD. 1009-1028
  
• Steve Moyle:
  Collaborative Data Mining. 1029-1039
  
• Hamid R. Nemati, Christopher D. Barko:
  Organizational Data Mining. 1041-1048
  
• Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn J. Keogh, Michail Vlachos, Gautam Das:
  Mining Time Series Data. 1049-1077
  

Part 7 / Applications

• Zhongfei (Mark) Zhang, Ruofei Zhang:
  Multimedia Data Mining. 1081-1109
  
• Nada Lavrac, Blaz Zupan:
  Data Mining in Medicine. 1111-1136
  
• Gautam B. Singh:
  Learning Information Patterns in Biological Databases - Stochastic Data Mining. 1137-1152
  
• Boris Kovalerchuk, Evgenii Vityaev:
  Data Mining for Financial Applications. 1153-1169
  
• Anoop Singhal, Sushil Jajodia:
  Data Mining for Intrusion Detection. 1171-1180
  
• Kurt H. Thearling:
  Data Mining for CRM. 1181-1188
  
• Nissan Levin, Jacob Zahavi:
  Data Mining for Target Marketing. 1189-1220
  
• Oded Maimon, Abel Browarnik:
  NHECD - Nano Health and Environmental Commented Database. 1221-1241
  

Part 8 / Software

• Qingyu Zhang, Richard S. Segall:
  Commercial Data Mining Software. 1245-1268
  
• Eibe Frank, Mark Hall, Geoffrey Holmes, Richard Kirkby, Bernhard Pfahringer, Ian H. Witten, Len Trigg:
  Weka-A Machine Learning Workbench for Data Mining. 1269-1277
  

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