Frequent patterns minning of stock data using hybrid clustering association algorithm

B., Baharudin and A., Khan and K., Khan (2009) Frequent patterns minning of stock data using hybrid clustering association algorithm. In: 2009 International Conference on Information Management and Engineering, ICIME 2009, 3 April 2009 through 5 April 2009, Kuala Lumpur.

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Patterns and classification of stock or inventory data is very important for business support and decision making. Timely identification of newly emerging trends is also 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 research 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 Fast-Moving (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. © 2009 IEEE.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Clustering; Dead-stock (DS); Fast-moving (FM); Most frequent patterns (MFP); Slow-moving(SM); Competition; Decision making; Dielectric relaxation; Information management; Strategic planning; Clustering algorithms
Subjects:Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments / MOR / COE:Departments > Computer Information Sciences
ID Code:180
Deposited By: Dr Baharum Baharudin
Deposited On:24 Feb 2010 14:57
Last Modified:19 Jan 2017 08:25

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