نوع مقاله : یادداشت فنی
نویسندگان
دانشکدهی مهندسی صنایع و مکانیک، دانشگاه آزاد اسلامی واحد قزوین
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
In recent years, data importance is widely considered as a resource with high information potential. Data mining is a process of extracting and refining knowledge from a large database. The extracted information can be used to
predict, classify, model, and characterize the data being mined. It is an intelligent method of discovering unknown or unexplored relationships within a large database. It uses the principles of pattern recognition and machine learning to discover knowledge, and various statistical and visualization techniques to present the knowledge in a comprehensible form. Data mining with extraction, and rapid and precise discovery of valuable and hidden information from data bases, is used for decision making and decision support. It is a technique that every country, organization and company requires in order for scientific, technological and economic development. Nowadays, considering the strong competition condition of companies and organizations to gain new
customers and maintain previous customers, the volume of customer information and dramatically complex interaction with customers, data mining has been a pioneer for acquisition profitability in customer relationships, considering the necessity to use data mining, especially clustering. In this paper, the first concept of clustering and its applications is considered, then a review about data mining mathematical concepts and clustering, including: minimizing the sums of squares within clustering, components related to p-dimension Euclidean space, minimizing the distances of the mean squared sum within clustering, number of clusters, minimizing the total distance within clustering, and minimizing the maximum distance within clusters, is addressed. A model of minimizing the mean sum of distances within clusters in customer segmentation is proposed. The proposed model is formulated as a mathematical model with the objective of minimizing the mean sum of distances within
clusters. Then, the model is compared with the minimizing the distances mean squared sum within clustering model using MATLAB 7.5.0 (R2007b). The proposed method does not depend on any initial positions for the cluster centers and does not allow any empirically adjustable parameters. In the tested cases, the model has improved the distances within clusters. Finally, the performance of the model is tested on a real problem for classification of the Parskhazar Industry customers. Experiments reveal that the proposed model has efficient
yield.
کلیدواژهها [English]
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