文章基本信息

标题：Application of Data Mining Tools for Selected Scripts of Stock Market
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作者：K. S. Mahajan ; Dr. R. V. Kulkarni
期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2014
卷号：4
期号：4
页码：55
DOI：10.5121/ijdkp.2014.4405
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：One of the most important problems in modern finance is finding efficient ways to summarize and visualizethe stock market data to give individuals or institutions useful information about the market behavior forinvestment decisions Therefore, Investment can be considered as one of the fundamental pillars of nationaleconomy. So, at the present time many investors look to find criterion to compare stocks together andselecting the best and also investors choose strategies that maximize the earning value of the investmentprocess. Therefore the enormous amount of valuable data generated by the stock market has attractedresearchers to explore this problem domain using different methodologies. Therefore research in datamining has gained a high attraction due to the importance of its applications and the increasing generationinformation. So, Data mining tools such as association rule, rule induction method and Apriori algorithmtechniques are used to find association between different scripts of stock market, and also much of theresearch and development has taken place regarding the reasons for fluctuating Indian stock exchange.But, now days there are two important factors such as gold prices and US Dollar Prices are moredominating on Indian Stock Market and to find out the correlation between gold prices, dollar prices andBSE index statistical correlation is used and this helps the activities of stock operators, brokers, investorsand jobbers. They are based on the forecasting the fluctuation of index share prices, gold prices, dollarprices and transactions of customers. Hence researcher has considered these problems as a topic forresearch.KEYWORDS
关键词：Stock Market; Association Rules; Rule Induction Methods; Apriori Algorithm; Correlation; Data Mining.