Prediction of Data Sets by using Fuzzy Mining Association Rule
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 5
Abstract
This paper is proposed to reinforce fuzzy association rules, Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. However, in real life quantitative data are voluminous and need careful attention for discovering knowledge. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. This problem will be solved by standard deviation and mean of the given data sets. The boundary values of the entire linguistic variable are calculated by the first phase of our algorithm.
Authors and Affiliations
Sushmita Acharjee, Rohit Miri, Asha Miri
Keywords
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- EP ID EP20659
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