Parallel Closed Frequent Pattern Mining on PC Cluster

Transcription

1 DEWS2005 3C-i5 PC, FPclose PC 32 PC 2% 30.9 PC Parallel Closed Frequent Pattern Mining on PC Cluster Eigo IWAHASHI, Yuu HIRATE, and Hayato YAMANA, School of Science and Enginnering, Waseda Universityhskip1em Okubo 3-4-1, Shinjuku-ku, Tokyo, Japan Science and Enginnering, Waseda Universityhskip1em Okubo 3-4-1, Shinjuku-ku, Tokyo, Japan National Institute of Informatics Hitotsubashi, Chiyoda-ku, Tokyo Japan Abstract Frequent patterns mining is one of the important problem in data mining research. Since frequent pattern mining processes very huge data, frequent pattern mining faces the lack of memory spaces or the increase of disk access. For the purpose of mining frequent patterns in real time by lowering such resource constraints, various parallel algorithms are proposed. However, since many of traditional parallel algorithms mine all frequent patterns, a large number of patterns are mined as a result. In this paper, we propose the parallel closed frequent pattern mining method besed of the FP-growth algorithm. In addition, we propose the load balancing method, which is indispensable to parallel methods. As a result of the evaluation using 32 node PC cluster, our method is approximately 12 times faster than sequential FPclose, when minimum support is 1.0%. In addition, our method copes with data scalability. Key words Data Mining, Frequent Pattern Mining, PC Cluster, Parallelization 1. Web Web DNA 1

2 Apriori [9] FP-growth [5] [6] [2] PC Apriori [10] [12] FP-growth [4] FPclose PC [9] I = {i 1,i 2,,i m} T = {t 1,t 2,,t n t i = I} T t i X support(x) T X T CFI : ClosedF requentitemset [6] [8] P P 1 P P 2 P P P P CFI 2. 2 FP-tree FP-growth 2000 Han FP-growth [5] FP-growth FP-tree FP-tree FP-tree FP-tree 1 F-list null T 1-item FP-tree 2 FP-tree 1 F-list 2 T F-list F-list 1 2 ID a i a i FP-tree FP-tree FP-growth FP-growth FP-tree 2 1 a i a i a i a i 2 P a i P a i prefix-path prefix-path a i m prefix-path m m m FP-tree FP-growth m 2. 3 FPclose FP-growth 2

3 2003 Grahne FPclose [2] FPclose FPgrowth FPclose Closed Pattern Mining [1] FPclose FPgrowth FP-tree FP-tree CFI-tree(Closed Frequent Itemset tree) CFI 2. 4 FP-growth PC Apriori [10], [12] FP-tree 2003 Iko PC FP-growth [4] 1 F-list 2 FP-tree FP-tree [4] 3. FPclose FPclose Closed Pattern Mining FPclose CFI FPclose FPgrowth CFI FPclose FP-growth [4] 3. 1 FP-tree p p FP-tree [10] [4] TDB p i p i TDB i FP-tree FPT i TDB 1-item p i p i TDB i FP-growth F-list FP-tree FP-tree 2 TDB FP-tree F-list F-list FP-tree 1 root root 1. FP-tree FP-tree 3. 2 FPT i CPB i CPB i TDB p i CPB i CPB i [4] 3

4 FPT i TDB i FPT i TDB [4] 1 FPT i PU α 1 α Flist 3. 3 CFI CFI-tree CFI CFI-tree X CFI-tree X X CFI X X 2 CFI MPI PC FPclose CFI-tree FP-tree. 1 Parallel FPclose CFI FPclose PC Intel Pentium4 2.40GHz 1GB(512MB 2) 1000Mbps MPICH(Version 1.2.5) IBM [3] 4. 2 FPclose T10I4D100k T10I4D500k T10I4D1000k % 1.5% 1.0% 0.5% 1 PU T10I4D100k 3 T10I4D500k 4 T10I4D1000k 2 4PU PU 4

5 2 PU 100k 4 PU 1000k 3 PU 500k PU 3 500k 16PU k 32PU IBM [3] T10I4D100k T10I4D500k T10I4D1000k T10I4D5000k T10I4D10000k 5 2% PU PU CFI 1000k 5000k 50 8PU 1000k 5000k 5 PC 5

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