| 書目名稱 | Mining Sequential Patterns from Large Data Sets | | 編輯 | Wei Wang,Jiong Yang | | 視頻video | http://file.papertrans.cn/635/634666/634666.mp4 | | 概述 | Contains unique multiple models for various sequential patterns and detailed algorithms on how to mine these patterns..Bio-informaticians not yet familiar with data mining models and algorithms will a | | 叢書名稱 | Advances in Database Systems | | 圖書封面 |  | | 描述 | In many applications, e.g., bioinformatics, web access traces, system u- lization logs, etc., the data is naturally in the form of sequences. It has been of great interests to analyze the sequential data to find their inherent char- teristics. The sequential pattern is one of the most widely studied models to capture such characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. In this book, we focus on sequential pattern mining. To meet different needs of various applications, several models of sequential patterns have been proposed. We do not only study the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. The objective of this book is to provide computer scientists and domain - perts such as life scientists with a set of tools in analyzing and understanding the nature of various sequences by : (1) identifying the specific model(s) of - quential patterns that are most suitable, and (2) providing an efficient algorithm for mining these patterns. Chapter 1 INTRODUCTION Data Mining is the process of extracti | | 出版日期 | Book 2005 | | 關(guān)鍵詞 | Mathematica; algorithms; bioinformatics; computer science; navigation; data structures | | 版次 | 1 | | doi | https://doi.org/10.1007/b104937 | | isbn_softcover | 978-1-4419-3707-0 | | isbn_ebook | 978-0-387-24247-7Series ISSN 1386-2944 | | issn_series | 1386-2944 | | copyright | Springer-Verlag US 2005 |
The information of publication is updating
|
|
|Archiver|手機(jī)版|小黑屋|
派博傳思國際
( 京公網(wǎng)安備110108008328)
GMT+8, 2025-10-8 01:04
發(fā)表于 2025-3-21 17:38:56
