A Comparative Study of Association Rule Algorithms for Investment in Related Sector of Stock Market | Zendy

Rajeev Kumar | Zendy; Arvind Kalia | Zendy

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A Comparative Study of Association Rule Algorithms for Investment in Related Sector of Stock Market

Author(s) -

Rajeev Kumar,

Arvind Kalia

Publication year - 2013

Publication title -

international journal of computer applications

Language(s) - English

Resource type - Journals

ISSN - 0975-8887

DOI - 10.5120/10119-4793

Subject(s) - computer science , association rule learning , stock market , stock (firearms) , association (psychology) , algorithm , data mining , mechanical engineering , paleontology , philosophy , epistemology , horse , engineering , biology

tment in the related stocks in share market plays vital role for investors. Variation in stock price is the barometer of growth of companies/sectors. Association Rule mining is one of the fundamental research topics in data mining and knowledge discovery that identifies interesting relationships between itemsets and predicted the associative and correlative behaviour for new data. In the present study the data of different stocks from National Stock Exchange of India Limited has taken and tried to find out the related stocks through Weka 3.6.5 data mining tool. In this paper four association rule algorithms: Apriori Association Rule, Predictive Apriori Association Rule, Tertius Association Rule and Filtered Associator were considered and the results of these four algorithms presented at different support and confidence level. It was found that Apriori Association Rule provided better results than other algorithms for selection of related stocks for investment in share market.

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