Sheng-Kun Hwang

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Mining frequent itemsets over data streams is an emergent research topic in recent years. Previous approaches generally use a fixed support threshold to discover the patterns in the stream. However, the threshold will be changed to cope with the needs of the users and the characteristics of the incoming data in reality. Changing the threshold implies a(More)
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(More)
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