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Unsupervised Learning Algorithms
M. Emre Celebi (Ed.)
€ 135.80
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Description for Unsupervised Learning Algorithms
Hardback. Editor(s): Celebi, M. Emre; Aydin, Kemal. Num Pages: 560 pages, 59 black & white illustrations, 101 colour illustrations, 71 black & white tables, 100 co. BIC Classification: UYQM. Category: (P) Professional & Vocational. Dimension: 235 x 155. .
This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms ... Read more
This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms ... Read more
Product Details
Publisher
Springer International Publishing AG
Format
Hardback
Publication date
2016
Condition
New
Weight
1001g
Number of Pages
558
Place of Publication
Cham, Switzerland
ISBN
9783319242095
SKU
V9783319242095
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
Reviews for Unsupervised Learning Algorithms
The book provides a valuable survey of an area of both research and application, particularly as massive datasets have become available. ... The book can be recommended to anyone interested in getting an overview of this fast-moving research and application area. Because each chapter has a comprehensive bibliography, the book can serve as an entry point for those wishing to ... Read more