Keywords: data mining, frequent pattern, frequent itemset, FP-tree, transaction database
Received February 28, 2011; revised August 21, 2011; accepted August 28, 2011.
Communicated by I-Chen Wu.
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SHOW-JANE YEN1, CHIU-KUANG WANG1,2 AND LIANG-YUH OUYANG2
1Department of Computer Science and Information Engineering
Ming Chuan University
Taoyuan County, 333 Taiwan
2Department of Management Sciences
Tamkang University
New Taipei City, 251 Taiwan
Mining frequent patterns is to discover the groups of items appearing always together
excess of a user specified threshold. Many approaches have been proposed for mining frequent
patterns by applying the FP-tree structure to improve the efficiency of the FP-Growth
algorithm which needs to recursively construct sub-trees. Although these approaches do
not need to recursively construct many sub-trees, they also suffer the problem of a large
search space, such that the performances for the previous approaches degrade when the
database is massive or the threshold for mining frequent patterns is low. In order to reduce
the search space and speed up the mining process, we propose an efficient algorithm for
mining frequent patterns based on frequent pattern tree. Our algorithm generates a subtree
for each frequent item and then generates candidates in batch from this sub-tree. For
each candidate generation, our algorithm only generates a small set of candidates, which
can significantly reduce the search space. The experimental results also show that our algorithm
outperforms the previous approaches.
Received February 28, 2011; revised August 21, 2011; accepted August 28, 2011.
Communicated by I-Chen Wu.