Charu C. Aggarwal is a research scientist at the IBM T.J. Watson Research Center. A fellow of the IEEE and the ACM, he is the author/editor of ten books, an associate editor of several journals, and the vice-president of the SIAM Activity Group on Data Mining. Dr. Aggarwal has published over 200 papers, has applied for or been granted over 80 patents, and has received numerous honors, including the IBM Outstanding Technical Achievement Award and EDBT 2014 Test of Time Award. His research interests include performance analysis, databases, and data mining. He earned a Ph.D. from the Massachusetts Institute of Technology.

• An Introduction to Data Classification. Feature Selection for Classification: A Review. Probabilistic Models for Classification. Decision Trees: Theory and Algorithms. Rule-Based Classification. Instance-Based Learning: A Survey. Support Vector Machines. Neural Networks: A Review. A Survey of Stream Classification Algorithms. Big Data Classification. Text Classification. Multimedia Classification. Time Series Data Classification. Discrete Sequence Classification. Collective Classification of Network Data. Uncertain Data Classification. Rare Class Learning. Distance Metric Learning for Data Classification. Ensemble Learning. Semi-Supervised Learning. Transfer Learning. Active Learning: A Survey. Visual Classification. Evaluation of Classification Methods. Educational and Software Resources for Data Classification. Index.

Charu C. Aggarwal is a research scientist at the IBM T.J. Watson Research Center. A fellow of the IEEE and the ACM, he is the author/editor of ten books, an associate editor of several journals, and the vice-president of the SIAM Activity Group on Data Mining. Dr. Aggarwal has published over 200 papers, has applied for or been granted over 80 patents, and has received numerous honors, including the IBM Outstanding Technical Achievement Award and EDBT 2014 Test of Time Award. His research interests include performance analysis, databases, and data mining. He earned a Ph.D. from the Massachusetts Institute of Technology.

• An Introduction to Data Classification. Feature Selection for Classification: A Review. Probabilistic Models for Classification. Decision Trees: Theory and Algorithms. Rule-Based Classification. Instance-Based Learning: A Survey. Support Vector Machines. Neural Networks: A Review. A Survey of Stream Classification Algorithms. Big Data Classification. Text Classification. Multimedia Classification. Time Series Data Classification. Discrete Sequence Classification. Collective Classification of Network Data. Uncertain Data Classification. Rare Class Learning. Distance Metric Learning for Data Classification. Ensemble Learning. Semi-Supervised Learning. Transfer Learning. Active Learning: A Survey. Visual Classification. Evaluation of Classification Methods. Educational and Software Resources for Data Classification. Index.

Charu C. Aggarwal is a research scientist at the IBM T.J. Watson Research Center. A fellow of the IEEE and the ACM, he is the author/editor of ten books, an associate editor of several journals, and the vice-president of the SIAM Activity Group on Data Mining. Dr. Aggarwal has published over 200 papers, has applied for or been granted over 80 patents, and has received numerous honors, including the IBM Outstanding Technical Achievement Award and EDBT 2014 Test of Time Award. His research interests include performance analysis, databases, and data mining. He earned a Ph.D. from the Massachusetts Institute of Technology.

• An Introduction to Data Classification. Feature Selection for Classification: A Review. Probabilistic Models for Classification. Decision Trees: Theory and Algorithms. Rule-Based Classification. Instance-Based Learning: A Survey. Support Vector Machines. Neural Networks: A Review. A Survey of Stream Classification Algorithms. Big Data Classification. Text Classification. Multimedia Classification. Time Series Data Classification. Discrete Sequence Classification. Collective Classification of Network Data. Uncertain Data Classification. Rare Class Learning. Distance Metric Learning for Data Classification. Ensemble Learning. Semi-Supervised Learning. Transfer Learning. Active Learning: A Survey. Visual Classification. Evaluation of Classification Methods. Educational and Software Resources for Data Classification. Index.