Author : Kritika Walinjkar, Fiyansh Shah and Sonal Maskeen

Cataloguing and patterns extraction from customer data is very important for business support and decision making. Timely recognition of newly emerging trends is needed in business process. Changing market trends need to be taken into consideration for predicting which products have more demand. This paper is about integrating two different algorithms, one is clustering algorithm, which is K-means and other is to find most frequent pattern i.e MFP which will help the back end of a company i.e production and inventory management unit to understand what product is selling more and which has a slow selling rate. In this way company can increase their profit by stocking the market with only those products that people buy.

Keywords: K-Means, Most Frequent Pattern (MFP), Data mining.

Article published in International Journal of Current Engineering and Technology, Vol.5, No.1 (Feb-2015)