Clustering Based Automatic Fuzzy Partitioning of Numerical Attributes
نویسنده
چکیده
Partitioning of quantified attributes is essential for mining association rules from quantified data and the Fuzzy approach solves the sharp boundary problem giving Fuzzy association rules having high interpretability and rich applicability. The paper presents automated partitioning of numerical data into Fuzzy sets based on k means clustering algorithm. This can be used as a pre-processing step for numerical data and as first step to Fuzzy Apriori algorithm used to generate Fuzzy Association rules. The algorithm also requires the definitions of fuzzy support and fuzzy cardinality of the dataset which are defined and used in validating the proposed technique over standard datasets.
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