Inducing Fuzzy Association Rules with Multiple Minimum Supports for Time Series Data

Technological changes have occurred at an exponential rate in recent years leading to the generation of large amount of data in various sectors. Several database and data warehouse is built to store and manage the data. As we know the data which are relevant to us should be extracted from the database for our task. Earlier different mining approaches are proposed in which items are collected at same minimum support value. In this paper we propose a fuzzy data mining algorithm which generates the fuzzy association rules from time series data having different minimum support values. The temperature varying dataset is used to generate fuzzy rules. The proposed algorithm also predicts the variation of temperature. Experiments are also performed to get the desired result.