Mining Positive and Negative Sequential Patterns in a Progressive Database

• Main Authors: Yong-BinWu, 吳永斌
• Other Authors: Jen-Wei Huang
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/52261662315279252235

碩士 === 國立成功大學 === 電腦與通信工程研究所 === 104 === Positive sequential pattern (PSP) mining focuses on appearing items, while negative sequential pattern (NSP) mining tends to find the relationship between occurring and non-occurring items. There are few works involved in NSP mining, and the definitions of NSP are inconsistent in each work. The support threshold for PSP is always applied on NSP, which cannot bring out interesting patterns. In addition, PSP has been discovered on incremental databases and progressive databases, while NSP mining is only performed on static databases. Progressive sequential pattern mining finds the most up-to-date patterns, which can provide more valuable information. However, the previous progressive sequential pattern mining algorithm contains some redundant process. In this paper, we aim to find NSP on progressive databases. A new definition of NSP is given to discover more meaningful and interesting patterns. We propose an algorithm, Propone, for efficient mining process. We also propose a lever-order traversal strategy and a pruning strategy to reduce the calculation time and the number of negative sequential candidates (NSC). By comparing Propone with some modified previous algorithms, the experimental results show that Propone outperforms comparative algorithms.