Data Mining And Analysis - Fundamental Concepts And Algorithms - Mohammed Zaki

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Résumé :
The fundamental algorithms in data mining and analysis are the basis for business intelligence and analytics, automated methods to analyze patterns, and models of data. This textbook for senior undergraduate and graduate data mining courses provides a comprehensive overview from an algorithmic perspective, integrating concepts from machine learning and statistics.

Biographie:
Mohammed J. Zaki is a Professor of Computer Science at Rensselaer Polytechnic Institute. He received his PhD in computer science from the University of Rochester in 1998. His research interests focus on developing novel data mining techniques, especially for applications in bioinformatics and social networks. He has published over 225 papers and book chapters on data mining and bioinformatics, and is the founding co-chair for the BIOKDD series of workshops. He is currently Area Editor for Statistical Analysis and Data Mining, and an Associate Editor for Data Mining and Knowledge Discovery, ACM Transactions on Knowledge Discovery from Data, and Social Network Analysis and Mining. He was the program co-chair for SDM'08, SIGKDD'09, PAKDD'10, BIBM'11, CIKM'12, and ICDM'12. He is currently serving on the Board of Directors for ACM SIGKDD. He received the National Science Foundation CAREER Award in 2001 and the Department of Energy Early Career Principal Investigator Award in 2002. He received an HP Innovation Research Award in 2010, 2011, and 2012, and a Google Faculty Research Award in 2011. He is a senior member of the IEEE, and an ACM Distinguished Scientist. His research is supported in part by NSF, NIH, DOE, Google, HP, and Nvidia. Wagner Meira, Jr is a Professor of Computer Science at the Universidade Federal de Minas Gerais, Brazil.

Sommaire:
1. Data mining and analysis; Part I. Data Analysis Foundations: 2. Numeric attributes; 3. Categorical attributes; 4. Graph data; 5. Kernel methods; 6. High-dimensional data; 7. Dimensionality reduction; Part II. Frequent Pattern Mining: 8. Itemset mining; 9. Summarizing itemsets; 10. Sequence mining; 11. Graph pattern mining; 12. Pattern and rule assessment; Part III. Clustering: 13. Representative-based clustering; 14. Hierarchical clustering; 15. Density-based clustering; 16. Spectral and graph clustering; 17. Clustering validation; Part IV. Classification: 18. Probabilistic classification; 19. Decision tree classifier; 20. Linear discriminant analysis; 21. Support vector machines; 22. Classification assessment.