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- W2550666578 abstract "Association rule mining is used to find association relationships among large data sets. Mining frequent patterns is an important aspect in association rule mining. Finding frequent itemsets in databases is crucial in data mining for purpose of extracting association rules. Many algorithms were developed to find those frequent itemsets. Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns. The FP-growth algorithm is currently one of the fastest approaches to frequent item set mining. In this paper, we proposed to compress the frequent pattern set mined from a transaction database to a compact set. The compact set is useful in application where the longest pattern is usually used." @default.
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- W2550666578 date "2016-01-01" @default.
- W2550666578 modified "2023-09-25" @default.
- W2550666578 title "Improved Association Rule Mining with Positive and Negative Integration" @default.
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