Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(323.89KB)
This is an Open Access publication published under CSC-OpenAccess Policy.

PUBLICATIONS BY COUNTRIES

Top researchers from over 74 countries worldwide have trusted us because of quality publications.

United States of America
United Kingdom
Canada
Australia
Malaysia
China
Japan
Saudi Arabia
Egypt
India
Efficient Mining of Association Rules in Oscillatory-based Data
Mohammad Saniee Abadeh, Mojtaba Ala
Pages - 195 - 207     |    Revised - 01-11-2011     |    Published - 15-12-2011
Volume - 2   Issue - 5    |    Publication Date - November / December 2011  Table of Contents
MORE INFORMATION
KEYWORDS
Documents Classification, Conceptual Graph, SVM
ABSTRACT
Association rules are one of the most researched areas of data mining. Finding frequent patterns is an important step in association rules mining which is very time consuming and costly. In this paper, an effective method for mining association rules in the data with the oscillatory value (up, down) is presented, such as the stock price variation in stock exchange, which, just a few numbers of the counts of itemsets are searched from the database, and the counts of the rest of itemsets are computed using the relationships that exist between these types of data. Also, the strategy of pruning is used to decrease the searching space and increase the rate of the mining process. Thus, there is no need to investigate the entire frequent patterns from the database. This takes less time to find frequent patterns. By executing the MR-Miner (an acronym for “Math Rules-Miner”) algorithm, its performance on the real stock data is analyzed and shown. Our experiments show that the MR-Miner algorithm can find association rules very efficiently in the data based on Oscillatory value type.
CITED BY (1)  
1 Ulagapriya, S., & Balasubramanian, P. (2015, August). Study on inter sector association rules in national stock exchange, India. In Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on (pp. 859-865). IEEE.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 R. Agrawal, T. Imielienski and A. Swami. “Mining Association Rules between Sets of Items in Large Databases”, in Proc. Management of Data, 1993, pp 207–216.
2 R. Agrawal, R. Srikant, “Fast Algorithm for Mining Association Rules”, in Proc. VLDB,Santiago de chile, 1994.
3 C. Borgelt and X. Yang, “Finding Closed Frequent Item Sets by Intersecting Transactions”,in Proc. 14th Int. Conf. on Extending Database Technology (EDBT 2011, Uppsala,Sweden), 2011.
4 J. Han, H. Pei and Y. Yin, “Mining Frequent Patterns without Candidate Generation”. In Proc. the Management of Data (SIGMOD'00, Dallas, TX), New York, NY, USA, ACM Press, pp. 1-12, 2000.
5 M. Zaki, S. Parthasarathy, M. Ogihara and W. Li, “New Algorithms for Fast Discovery of Association Rules”, In Proc. 3rd Int. Conf. on Knowledge Discovery and Data Mining(KDD'97, Newport Beach, CA), AAAI Press, pp. 283-296, 1997.
6 Claudio Lucchese, Salvatore and Raffaele Perego, “Fast and Memory Efficient Mining of Frequent Closed Itemsets”, in Proc Knowledge and Data Engineering, pp. 21-36, 2006.
7 Anthony J.T. Lee and Chun-Sheng Wang, “An Efficient Algorithm for Mining Frequent InterTransaction Patterns”, Information Sciences 177, pp. 3453-3476, 2007.
8 Shu-hsien Liao, Pei-hui Chu and Tzu-kang Teng, “Mining the co-movement in the Taiwan Stock Funds Market”, Expert Systems with Applications, Volume 38, Issue 5, pp. 5276-5288, 2011.
9 Sung Hoon Na and So Young Sohn, “Forecasting Changes in Korea Composite Stock Price Index (KOSPI) using Association Rules”, Expert Systems with Applications, Volume 38, Issue 7, pp. 9046-9049, 2011.
10 Hulyi Tan Cai and H.J. Yong Li, “Frequent Patterns of Investment Behaviors in Shanghai Stock Market”, Science and Software Engineering, Volume 04, pp. 325-328, 2008.
11 Vladimir Boginski, Sergiy Butenko and Panos M. Pardalos, “Mining Market Data: A Network Approach”, Computers & Operations Research, Volume 33, Issue 11, pp. 3171-3184, 2006.
12 Borgelt, C., Yang, X., Nogales-Cadenas, R., Carmona-Saez, P., Pascual-Montano, A.“Finding Closed Frequent Item Sets by Intersecting Transactions”, In. 14th. Extending Database Technology, ACM Press, New York, pp. 367-376, 2011.
Associate Professor Mohammad Saniee Abadeh
Tarbiat Modares University - Iran
saniee@modares.ac.ir
Mr. Mojtaba Ala
- Iran