Clustering Stock Market Companies via K- Means Algorithm

One of the main concepts in pattern recognition is clustering. This technique is used as important knowledge discovery tools in modern machine learning process. Clustering of high-performance companies is very important not only for investors, but also for the creditors, financial creditors, stockholders, etc. Hence, firms’ clustering is considered as one of the important issues in Tehran Stock Exchange (TSE). To this end, we have used financial statement data of three industries in TSE for the year 2012. After selecting profit criteria (attributes) and prioritizing them using AHP, k means clustering algorithm is used to classify these companies. Also, to obtain the optimal number of clusters, different validity measures are presented. The identification of clusters of companies of TSE can be exploited to improve planning and get to more comprehensive decision making about companies.

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