Mining Customer Data for Decision Making Using New Hybrid Classification Algorithm

Classification and patterns extraction from customer data is very important for business support and decision making. Timely identification of newly emerging trends is needed in business process. Sales patterns from inventory data indicate market trends and can be used in forecasting which has great potential for decision making, strategic planning and market competition. The objectives in this paper are to get better decision making for improving sale, services and quality as to identify the reasons of dead stock, slow-moving, and fast-moving products, which is useful mechanism for business support, investment and surveillance. In this paper we proposed an algorithm for mining patterns of huge stock data to predict factors affecting the sale of products. In the first phase, we divide the stock data in three different clusters on the basis of product categories and sold quantities i.e. Dead-Stock (DS), Slow-Moving (SM) and FastMoving (FM) using K-means algorithm. In the second phase we have proposed Most Frequent Pattern (MFP) algorithm to find frequencies of property values of the corresponding items. MFP provides frequent patterns of item attributes in each category of products and also gives sales trend in a compact form. The experimental result shows that the proposed hybrid k-mean plus MFP algorithm can generate more useful pattern from large stock data.