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Journal of Information Science and Engineering, Vol. 32 No. 3, pp. 575-595 (May 2016)


Efficient Mining of Profit Rules from Closed Inter-Transaction Itemsets


YU-LUNG HSIEH1, DON-LIN YANG1,*, JUNGPIN WU2 AND YI-CHUNG CHEN1
1Department of Information Engineering and Computer Science
2Department of Statistics
Feng Chia University
Taichung, 40724 Taiwan
E-mail: yuhlong.hsieh@gmail.com; {dlyang; chenyic}@fcu.edu.tw1
E-mail: jungpinwu@gmail.com2

Data mining applications in financial sectors are very common since investors can apply the resultant rules to make profits. Profit mining algorithms in particular, such as PRMiner, can generate profit rules that meet the expectations of investors regarding profit, risk, and win rate. However, most of such algorithms are not efficient due to the long processing time involving going through the whole search space in complex dynamical systems of financial markets. Hence, we propose a new approach in this paper to solve the problem by using closed itemsets to obtain profit rules without processing the entire trading rules. Based on the inter-day modeling, we analyze inter-transactions and conduct trading simulations to predict trading results for efficient profit rule generation. We develop two algorithms of JCMiner and ATMiner to process closed itemsets, which have better performance than the approach of PRMiner, especially for the large number of itemsets and large datasets. According to the experimental results, our algorithms outperform PRMiner in various experimental scenarios, i.e., mining parameters, the number of items in a transaction, and the number of transactions in a dataset.

Keywords: financial data mining, association rule, closed itemset, inter-transaction mining, profit mining

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Received January 21, 2015; revised March 11 & April 26 & July 26, 2015; accepted August 30, 2015.
Communicated by Tzung-Pei Hong.
* Corresponding author.