Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/14276
Title: A fast algorithm for mining the longest frequent itemset
Authors: FU QIAN
Keywords: Data Mining, Frequent Itemsets, Clustering, FP-tree, Conditional Pattern Base.
Issue Date: 27-Oct-2004
Source: FU QIAN (2004-10-27). A fast algorithm for mining the longest frequent itemset. ScholarBank@NUS Repository.
Abstract: Mining frequent itemsets in databases has been popularly studied in data mining research. Most existing work focuses on mining frequent itemsets, frequent closed itemsets or maximal frequent itemsets. But as the database becomes huge and the transactions in the database become very large, it becomes highly time-consuming to mine even the maximal frequent itemsets. In this thesis, we define a new problem, finding only the longest frequent itemset from a transaction database, and present a novel algorithm, called LFIMiner (Longest Frequent Itemset Miner), to solve this problem. Longest frequent itemset can be quickly identified in even very large databases, and we find there are some real world cases where there is a need for finding the longest frequent itemset. LFIMiner generates the longest frequent itemset with pattern fragment growth, using a number of optimizations to prune the search space. A thorough experimental analysis indicates that LFIMiner is highly efficient for longest pattern mining and also has a good scalability.
URI: http://scholarbank.nus.edu.sg/handle/10635/14276
Appears in Collections:Master's Theses (Open)

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