Similarity measurement is a critical component in any case-based reasoning (CBR) system. CBR is
a superior technique for solving new problems based on previous experiences. Main assumption in
CBR relies on the hypothesis that states similar problems should have similar solutions. This paper
describes a comparative analysis on several commonly used similarity measures (Canberra, Clark, and Normalized Euclidean distance) in retrieving phase of the case-based reasoning approach to facilitate supplier selection. In addition, the proposed agent-based supplier selection framework was designed to use customer’s defined weights to evaluate the price, volume, quality grade, and delivery date of supply materials, and also provide them with alternative products which are closest to their first order if it was out of stock. Finally, based on the proposed framework, a numerical example of the used approach is illustrated.
K-Means is an unsupervised method partitions the input space into clusters. K-Means algorithm has a weakness of detecting outliers, which have it available in many variations research fields. A decade ago, Rough Sets Theory (RST) has been used to solve the problem of clustering partition. Specifically, Rough K-Means (RKM) is a one of the powerful hybrid algorithm, which has it, has various extension versions. However, with respect of the ideas of existing rough clustering algorithms, a suitable method to detect outliers is much needed now. In this paper, we propose an effective method to detect local outliers in rough clustering. The Local Outlier Factor (LOF) method in rough clustering improves the quality of the cluster partition. The improved algorithm increased the level of clusters quality. An existing algorithm version, the π Rough K-Means (π RKM) tested in the study. Finally, the effectiveness of the algorithm performance is demonstrated based on synthetic and real datasets.