关键词: 数据挖掘, 模糊逻辑, 时态序列演化模式

Abstract: Most existing temporal sequence mining methods depend on partitioning and discretization in a natural way，which brings on that the symbols of the alphabet are usually chosen externally and imposed by the users.In order to reduce the randomicity，the original temporal sequence is transformed into fuzzy form by fuzzy clustering，and then frequent fuzzy temporal evolution patterns are assessed with support and confidence measure.Rule’s support and confidence are calculated from membership and each sample does not arbitrarily support a single symbol so as to make the two important measures more exact and actual.An apriori algorithm and its complexity for discovering frequent fuzzy itemsets are present.The practical calculation shows that the mining of temporal sequence evolution patterns on commodity futures data is meaningful and resultful.

Key words: data mining, fuzzy logic, temporal sequence evolution patterns