Interval-based and Point-based Sequential Pattern Mining

• Main Authors: Shin-Yi Wu, 吳欣怡
• Other Authors: 陳彥良
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/65594804857311272425

博士 === 國立中央大學 === 資訊管理研究所 === 95 === Data mining is useful in various domains, such as market analysis, decision support, fraud detection and business management, among others. Many approaches have been proposed to extract information and sequential pattern mining is one of the mostimportant methods. Previous studies of sequential pattern mining have discovered patterns from point-based event sequences. However, in some applications, event sequences may contain interval-based events or hybrid events (both point-based and interval-based events). Frequent patterns discovered from interval-based event sequences are called temporal patterns, and those discovered from hybrid event sequences are called hybrid temporal patterns. But because the existing methods for discovering sequential patterns are not applicable to mine temporal pattern or hybrid patterns, this study is dedicated to develop new methods to discover temporal patterns and hybrid temporal patterns. Both proposed methods have been verified for efficiency and effectiveness by using synthetic and real datasets.