Data Clustering: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
| Author | : | |
| Rating | : | 4.83 (843 Votes) |
| Asin | : | 1466558210 |
| Format Type | : | paperback |
| Number of Pages | : | 652 Pages |
| Publish Date | : | 2017-08-22 |
| Language | : | English |
DESCRIPTION:
He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering Journal from 2004 to 2008. J. Aggarwal is a Research Scientist at the IBM T. Chandan K. He is an associate editor of the ACM TKDD Journal, an action editor of the Data Mining and Knowledge Discovery Journal, an associate editor of the ACM SIGKDD Explorations, and an associate editor of the Knowledge and Information Systems Journal. Watson Research Center in Yorktown Heights, New York. He is a member of IEEE, ACM, and SIAM. About the AuthorCharu C. He has served on the program committees of most major database/data mining conferences, and served as program vice-chairs of the SIAM Conference on Data Mining, 2007, the IEEE ICDM Conference, 2007, the WWW Conference 2009, and the IEEE ICDM Conference, 2009. His primary research interests are in the areas of data mini
Because of the commercial value of the above-mentioned patents, he has received several invention achievement awards and has thrice been designated a Master Inventor at IBM. He has served on the program committees of most major database/data mining conferences, and served as program vice-chairs of the
"Worth the read" according to Jonquille. Good collection of articles - freely accessible online.Check out Taming Text, Programming Collective Intelligence, and SciKit-Learnfor less mathematical, more road-ready, how-to with code.For more accessible reading/understanding, check out Stanford's Text Mining book (free online).. Must buy for Data Scientists who want to really understand clustering Robert J. Lake I have read this book twice. Can not put it down. As a practicing Data Scientist it has really helped me to tune my clustering algorithms. Well worth the money.
It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering:Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorizationDomains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clusterin