排序對關聯演算法速度的影響

何達明

doi:10.6840/cycu200200609

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排序對關聯演算法速度的影響

Partial order improve the performance of the association rule computing

何達明 , Masters　　Advisor：劉士豪　　

繁體中文 DOI： 10.6840/cycu200200609

關聯式規則；交易物項；頻物項集合；非高頻物項集合；資料挖掘； sortcompress ； fp-tree ； apriori ； association rules ； datamining

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Abstract

In recent years many business use computer to keep their transaction data and control their business process. Until now all Corp. has their own computers keep a lot’s of their process digital data. Faster and faster data generates. Those data conclude very important information and some useful knowledge , but we had no idea to use and discover it , but now the information science has a big improves, the faster computer, the larger size disk, the more popular database application, those let’s can find the knowledge hides in database.

The way to find knowledge in data is called “Data mining”. One data mining method is called “Association Rules”. A classical method “Apriori” has use on it for many years, It’s a way to look for Association Rules but it’s really not a good way, Apriori use the Psychology “Guess and filter” so spend many efforts on something no need to care about, so It’s need many main memory and compute time. Someone discovered a smart way to do it. Use a compressed tree to record the pattern with counts , so can in two pass database read to find whole all association rules. The compressed tree we call it “FP-Tree”. It’s a pretty nice method to find association rules. It’s brought a reduced complexity order and lower efforts. But it’s not made by the god. It’s need much memory to construct the FP-Tree. So it can hardly use on very large database, if it need to swap the tree between memory and disk. It’s may be a nightmare. The master idea of FP-Tree is compress transaction database into a limited main memory.
Main memory faster than disk for ten thousand times. So use it can reduce lot’s of efforts on swap and scan.

Now we find an new way with large compress ratio on transfer transaction data into to main memory can stored and can easy to use it to extracts association rules. We named this logarithm “SortCompress”. We use a compress array to keep the transaction association rules and use sort to help the compress process faster. It slower than FP-Tree a little, but it just need half size memory to stored it, so we can process more complexity transaction data use just an normal computer, and do it well. Now we will introduce that how we do it.

Keywords: datamining, association rules, apriori, fp-tree, sortcompress,

Reference ( 10 ) 〈TOP〉

[1] A. Savasere, E. Omiecinski, and S. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databases”, In Proceedings of 21st VLDB, pp.432-444,1995
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[2] D. Lin and Z. M. Kedem, “Pincer-Search: A New Algorithm for Discovering the Maximum Frequent Set”, In Proceedings of VI Intl. Conference on Extending Database Technology, 1998.
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[6] J.S.Park,M.S.Chen,and P.S.Ui,”An Effective Hash Based Algorithm for Mining Association Rules”, Proceedings of the ACM SIGMOD, pp.1750186,1995
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[7] M.Houtsma and A. Swami, “Set-Oriented Mining of Association Rules in Relational Databases,” 11th int’l Conference on Data Engineer, 1995
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