Verma, Navya and Rai, Shwetha and Pai, Vidya (2017) Discovery of Infrequent Weighted Itemsets using Tree Structure: A Comparison between FP-Tree and RP-Tree. In: Computer applications based on modern algebra, 01/07/2017, MIT Manipal.

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Abstract

Itemset Mining is a data mining technique used to extract useful information from data. Infrequent Itemset Mining is used to discover rare itemsets whose frequency of occurrence in the dataset is less than or equal to a maximum support threshold. Weighted Itemset gives local significance to items in each transaction. Weight is given to each item using w-support measure which uses hubs and authorities. Equivalent transactions are generated corresponding to each transaction. Frequent Pattern Tree is constructed using the equivalent transactions and Infrequent Weighted Itemset Mining algorithm is used to mine the infrequent weighted itemsets. Rare Pattern Tree algorithm is used to construct the Rare Pattern Tree using transactions which consists of rare items and Infrequent Weighted Itemset Mining algorithm is used to mine the infrequent weighted itemsets. A comparison between Frequent Pattern algorithm and Rare Pattern Tree algorithm is done and results are analyzed.

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