An approach to mining bundled commodities
Knowledge-Based Systems
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Association rules method is important in mining large sets of data, but there are often many meaningless rules discovered, which affects the algorithm's efficiency, moreover, the presentation of rules is not easy for users to understand. User guide often works during association rules mining procedure. This paper presents a user-guided association rules mining method, considering users' differential emphasis on each item through fuzzy regions. This is more realistic and practical than prior association rules methods. Moreover, the discovered rules are expressed in natural language that is more understandable to humans. The paper finally uses the proposed method to analyze data sets of appliance loan, the performance of the proposed approach is demonstrated.