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| Efficient Mining of Association Rules in Oscillatory-based Data
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Source |
International Journal of Artificial Intelligence and Expert Systems (IJAE) |
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Table of Contents |
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Volume: 2 Issue: 5 |
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Pages: NULL |
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Publication
Date: November / December 2011 |
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ISSN
(Online): 2180-124X |
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Pages |
195 - 207 |
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Author(s) |
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Published
Date |
15-12-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
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KEYWORDS: Documents Classification, Conceptual Graph, SVM |
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| 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. |
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| Mohammad Saniee Abadeh : Colleagues
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| Mojtaba Ala : Colleagues
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