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About this product
- DescriptionWith the increasing advances in hardware techlogy for data collection, and advances in software techlogy (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions- the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagsis, earth science, web log analytics, and social network analysis are covered.
- Author(s)Charu C. Aggarwal
- PublisherSpringer-Verlag New York Inc.
- Date of Publication25/06/2015
- SubjectComputing: Professional & Programming
- Place of PublicationNew York
- Country of PublicationUnited States
- ImprintSpringer-Verlag New York Inc.
- Out-of-print date15/12/2016
- Content Note5 black & white tables, biography
- Weight706 g
- Width155 mm
- Height235 mm
- Spine24 mm
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