Association Rules Mining Using Majority Voting in the Stock Data
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Abstract
A time series data set consists of sequence of values or events that change with time. Stock data mining plays an important role to
visualize the behavior of financial market. Every investor wants to know or predict the trends of the stock trading. Association rule mining
algorithms can be used to discover all item associations (or rules) in a dataset that satisfy user-specified constraints, i.e. minimum support and
minimum confidence. The traditional association analysis is intra-transactional because it concerns items within the same transaction. Patterns
are evaluated in this paper by means of generating association rules with a majority voting approach. The rules having the same consequent and
higher voting are picked up to determine the stock pattern. The experimental results demonstrate notable similar pattern as well as
categorization of stocks. The pattern so generated helps investors to build their portfolio and use these patterns to learn more about investment
planning and financial market.
Keywords: Stocks data mining, association rules, majority voting
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