Abstract
Problem statement: Frequent itemset mining is an important task in data mining to discover the hidden, interesting associations between items in the database based on the user-specified support and confidence thresholds. Approach: In order to find important associations, an appropriate support threshold has to be specified. The support threshold plays a key role in deciding the interesting itemsets. The rare itemsets may not found if a high threshold is set. Some uninteresting itemsets may appear if a low threshold is set. Results: This study proposes an approach to obtain the frequent itemsets involving rare items by setting the support thresholds automatically. Experimental results show that this approach produces rare and frequent itemsets in sparse and dense datasets. According to T20I6D100K, 97.76% of the FIs are generators wherein Mushrooms 1.38% of the FIs are the generators. Conclusion: The proposed algorithm produces both frequent and rare itemsets in an effective way. In future, computational efforts can still be reduced by implementing the algorithm as parallel algorithm.
Highlights
Data mining is widely used in a variety of application areas such as banking, marketing and retail industry
Frequent itemset mining is a technique used in data mining to discover hidden associations that arise between various data items (Agrawal et al, 1993)
The problem of finding rare or infrequent patterns has recently captured the interest of the data mining community
Summary
Data mining is widely used in a variety of application areas such as banking, marketing and retail industry. The market basket analysis is intended for discovering which items tend to be purchased together in order to detain the purchase behavior of customers and to improve business. The importance is being given for the discovery of infrequent or exceptional patterns like fraudulent credit card transactions, rare symptoms which leads to disease. Some sets of items, such as milk and bread, occur frequently and can be considered as regular cases. When compared to milk and bread, some items like a gold chain and a ring are infrequently associated itemsets, but considered to be an important association. The problem of finding rare or infrequent patterns has recently captured the interest of the data mining community
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