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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 sales, 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, the authors propose an algorithm for mining patterns of huge stock data to predict factors affecting the sale of products.
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