Abstract
The application of fuzzy logic in consumer behaviour using fuzzy sets can give a more realistic understanding for firms, marketing research agencies and the policy makers. In this paper, an algorithm fuzzy set-based frequent itemset mining (FSFIM) has been proposed to mine frequent patterns in the form of itemsets and association rules which are expressed in the form of categories of item. Itemsets in the source database are classified into low, medium and high based upon quantity of the item purchased in a transaction. The classification of an item is done using membership of the item to the fuzzy set where each category is represented as a fuzzy set. The number of patterns of interest generated using FSFIM is comparatively less as compared to the traditional methods.
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Amballoor, R.G., Naik, S.B. (2022). Fuzzy Set-Based Frequent Itemset Mining: An Alternative Approach to Study Consumer Behaviour. In: Agrawal, S., Gupta, K.K., Chan, J.H., Agrawal, J., Gupta, M. (eds) Machine Intelligence and Smart Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-9650-3_21
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