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- Publisher: Information Science Reference
- Year: 2011
- Pages: 286
- ISBN-10: 1613500564
- ISBN-13: 9781613500569
- Format: 21.6 x 27.9 x 1.8 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
Pattern Discovery Using Sequence Data Mining (e-book) (used book) | bookbook.eu
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Description
Sequential data from Web server logs, online transaction logs, and performance measurements is collected each day. This sequential data is a valuable source of information, as it allows individuals to search for a particular value or event and also facilitates analysis of the frequency of certain events or sets of related events. Finding patterns in sequences is of utmost importance in many areas of science, engineering, and business scenarios. Pattern Discovery Using Sequence Data Mining: Applications and Studies provides a comprehensive view of sequence mining techniques and presents current research and case studies in pattern discovery in sequential data by researchers and practitioners. This research identifies industry applications introduced by various sequence mining approaches.
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- Publisher: Information Science Reference
- Year: 2011
- Pages: 286
- ISBN-10: 1613500564
- ISBN-13: 9781613500569
- Format: 21.6 x 27.9 x 1.8 cm, hardcover
- Language: English English
Sequential data from Web server logs, online transaction logs, and performance measurements is collected each day. This sequential data is a valuable source of information, as it allows individuals to search for a particular value or event and also facilitates analysis of the frequency of certain events or sets of related events. Finding patterns in sequences is of utmost importance in many areas of science, engineering, and business scenarios. Pattern Discovery Using Sequence Data Mining: Applications and Studies provides a comprehensive view of sequence mining techniques and presents current research and case studies in pattern discovery in sequential data by researchers and practitioners. This research identifies industry applications introduced by various sequence mining approaches.
Reviews