Top-K Sequential Pattern Mining in the Data Stream

• Main Authors: Hung-lin Jiang, 姜弘霖
• Other Authors: Bi-Ru Dai
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/42334633626005499693

碩士 === 國立臺灣科技大學 === 資訊工程系 === 97 === Sequential pattern mining is a process of extracting useful patterns in data sequences. A popular example is web log access sequences. Proprietors usually use such information to find the habits of the users by observing the web pages that they often surf to perform commercial activities. Existing works on mining Top-K patterns on data streams are mostly for non-sequential patterns. In our framework, we focus on the topic of Top-K sequential pattern mining, where users can obtain adequate amount of interesting patterns. The proposed method can automatically adjust the minimum support during mining each batch in the data stream and obtain candidate patterns. Then candidate patterns are maintained by a tree structure for extracting Top-K sequential patterns. Empirical results show that the proposed method is efficient and scalable.