Interactive Mining of Frequent Itemsets over Arbitrary Time Intervals in a Data Stream

Mining frequent patterns in a data stream is very challenging for the high complexity of managing patterns with bounded memory against the unbounded data. While many approaches assume a fixed support threshold, a changeable threshold is more realistic, considering the rapid updating of the streaming transactions in practice. Additionally, mining of itemsets over various time granularities rather than over the entire stream may provide more flexibility for many applications. Therefore, we propose a interactive mechanism to perform the mining of frequent itemsets over arbitrary time intervals in the data stream, allowing a changeable support threshold. A synopsis vector having tilted-time tables is devised for maintaining statistics of past transactions for support computation over user-specified time periods. The extensive experiments over various parameter settings demonstrate that our approach is efficient and capable of mining frequent itemsets in the data stream interactively, with...

• Type: Conference
• Year: 2008
• Where: ADC
• Authors: Ming-Yen Lin, Sue-Chen Hsueh, Sheng-Kun Hwang
• Keywords: ADC 2008, Data Stream, Database, Mining Frequent Patterns, Support Thresholds