Users are individuals: individualizing user models
International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
Adaptive Assistants for Customized E-Shopping
IEEE Intelligent Systems
Web usage mining based on probabilistic latent semantic analysis
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Segmentation of stock trading customers according to potential value
Expert Systems with Applications: An International Journal
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Clustering of retail stores in a distribution network with specific geographical limits plays an important and effective role in distribution and transportation costs reduction. In this paper, the relevant data and information for an established automotive spare-parts distribution and after-sales services company (ISACO) for a 3-year period have been analyzed. With respect to the diversity and lot size of the available information such as stores location, order, goods, transportation vehicles and road and traffic information, three effecting factors with specific weights have been defined for the similarity function: 1. Euclidean distance, 2. Lot size 3. Order concurrency. Based on these three factors, the similarity function has been examined through 5 steps using the Association Rules principles, where the clustering of the stores is performed using k-means algorithm and similar stores are allocated to the clusters. These steps include: 1. Similarity function based on the Euclidean distances, 2. Similarity function based on the order concurrency, 3. Similarity function based on the combination of the order concurrency and lot size, 4. Similarity function based on the combination of these three factors and 5. Improved similarity function. The above mentioned clustering operation for each 5 cases addressed in data mining have been carried out using R software and the improved combinational function has been chosen as the optimal clustering function. Then, trend of each retail store have been analyzed using the improved combinational function and along with determining the priority of the depot center establishment for every cluster, the appropriate distribution policies have been formulated for every cluster. The obtained results of this study indicate a significant cost reduction (32%) in automotive spare-parts distribution and transportation costs.