Mining Association Rules From Time Series Data Using Hybrid Approaches
نویسندگان
چکیده
Due to the frequent appearance of time series data in various fields, it has always been an essential and interesting research field. A time series analysis involves the methods for analyzing time series data, in order to mine meaningful and other relevant characteristics of the data. In most cases, time series data are quantitative values, so to come up with an intellectually appealing data mining algorithm to deal with quantitative type of data presents a biggest challenge to researchers in this field. In this paper, an extended Fuzzy Frequent Pattern (FP) growth approach is proposed and analyzed against the existing approach called Fuzzy Apriori (FA).
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