Fuzzy association rules mining from time series based on FCM clustering
Wang Zhao-fei · Dalian Ligong Daxue xuebao · 2010
On the occasion of dealing with time series from complex system,the investigation of series'local patterns and local relationship has distinct superiority over traditional global models.In order to find rules relating to inside or local patterns in a time series,fuzzy C-means(FCM)clustering is used to soften the effect of sharp boundaries of delegate of each local sub-series.Then,the parallel algorithm for mining Boolean association rules is improved to discover frequent fuzzy attributes set.Finally,the fuzzy association rules with least fuzzy confidence are parallelly generated by all processors.The practical calculation results show that the mining of fuzzy association rules from time series based on FCM clustering is effective.