1Assistant Professor, MCA Programme, Shri Jairambhai Patel Institute of Business Management and Computer Applications, Gandhinagar, Gujarat-382007, India
2Research Scholar, Shri Jairambhai Patel Institute of Business Management and Computer Applications, Gandhinagar, Gujarat-382007, India
3Director (I/C) & Associate Professor, Narmada College of Computer Application, Bharuch, Gujarat, India:
4Research Guide, Faculty of Science R.K. University, Rajkot, Gujarat, India
Email: preeti.dalal@gmail.com
Email: saini_expert@ yahoo.com
In today's competitive financial market, individuals want to earn profit from their investments. Stock market is a public market where an individual can buy/sell a share at some level of risk and earn maximum profit from the investments. There are various data mining techniques available, like classification, association rule mining, clustering and outlier analysis. Clustering is one of the data mining techniques used in various financial domains. The objective of this research paper is to construct clusters of Nifty companies for better investment. This paper shows analysis of Nifty companies using k-means algorithm. The k-means algorithm is one of the famous clustering techniques that generate clusters based on some investment criteria. In this paper, price per earnings ratio was considered as an investment criteria. These clusters guide the investors to invest their funds in different securities.
Clustering, k-Means, Price per earnings ratio, Square error, Self Organizing Map(SOM), Fuzzy C-means
