Modern Information Retrieval
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
On the performance of feature weighting K-means for text subspace clustering
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
IEEE Transactions on Knowledge and Data Engineering
Hybrid-LWM: A linear-model based hybrid clustering algorithm for supplier categorisation
International Journal of Systems, Control and Communications
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Many large enterprises work with thousands of suppliers to provide raw materials, product components and final products. Supplier relationship management (SRM) is a business strategy to reduce logistic costs and improve business performance and competitiveness. Effective categorization of suppliers is an important step in supplier relationship management. In this paper, we present a data-driven method to categorize suppliers from the suppliers’ business behaviors that are derived from a large number of business transactions between suppliers and the buyer. A supplier business behavior is described as the set of product items it has provided in a given time period, a mount of each item in each order, the frequencies of orders, as well as other attributes such as product quality, product arrival time, etc. Categorization of suppliers based on business behaviors is a problem of clustering high dimensional data. We used the k-means type subspace clustering algorithm FW-KMeans to solve this high dimensional, sparse data clustering problem. We have applied this algorithm to a real data set from a food retail company to categorize over 1000 suppliers based on 11 months transaction data. Our results have produced better groupings of suppliers which can enhance the company’s SRM.