Processing Sequential Patterns in Relational Databases

Database integration of data mining has gained popularity and its significance is well recognized. However, the performance of SQL based data mining is known to fall behind specialized implementation since the prohibitive nature of the cost associated with extracting knowledge, as well as the lack of suitable declarative query language support. Recent studies have found that for association rule mining and sequential pattern mining with carefully tuned SQL formulations it is possible to achieve performance comparable to systems that cache the data in files outside the DBMS. However most of the previous pattern mining methods follow the method of Apriori which still encounters problems when a sequential database is large and/or when sequential patterns to be mined are numerous and long. In this paper, we present a novel SQL based approach that we recently proposed, called Prospad (PROjection Sequential PAttern Discovery). Prospad fundamentally differs from an Apriori-like candidate s...

• Type: Conference
• Year: 2005
• Where: DAWAK
• Authors: Xuequn Shang, Kai-Uwe Sattler
• Keywords: Data Mining, DAWAK 2005, Mining, Sequential Pattern Mining, Sequential Patterns