Survey on Infrequent Weighted ItemsetMining Using FP Growth

[摘要] A new algorithm for infrequent item set mining, finding infrequent weighted item set in transaction database. Frequent weighted item sets represent correlation regularly holding in data in which items may weight differently. The research society has focused on the infrequent weighted item set mining problem. Infrequent weighted item set discover item sets whose frequency of occurrence in the analyzed data is less than or equal to a maximum threshold. To discover infrequent weighted item set, two algorithms are discovered Infrequent weighted item set IWI and Minimal infrequent item set MIWI. In this survey is focused on the infrequent weighted item sets, from transactional weighted data sets to address IWI support measure is defined as a weighted frequency of occurrence of an item set in the analyzed data. Occurrence weights derived from the weights associated with items in each transaction and applying a given cost function.

[关键词] clustering;classification;FP tree;FP growth;association rules;and data mining