means clustering

In this paper, a multi-level benchmarking method is presented for assessing efficiency of electrical distribution companies’ expenditures. Proposed method considers mutual cooperation of capital expenditure (CAPEX) and operational expenditure (OPEX) for improving quality and decreasing energy losses. Firstly, weighted fuzzy c-means clustering (WFCM) is used by proposed method to find similar companies. Then, proposed method utilizes data envelopment analysis (DEA) and slack analysis in multi levels and through creating pseudo competition between companies, estimates companies’ efficiency scores and slacks in their expenditures. Finally, proposed IBRS is applied on Iranian distribution companies and its results are discussed.

Clustering is one of the most widely used methods in data analysis. In the classical clustering methods it is assumed that all the features of the samples in a given data set make equal contribution when constructing the optimal clusters. However, in real- world datasets, some of the features can exhibit higher relevance than others. Thus, the features with higher relevance are more important to form optimal result than those with lower relevance. To address this issue, in this paper we proposed a feature weighted fuzzy clustering method called RLWFCM. The proposed algorithm has three main advantages. The first advantage is that our method assigns different weights to each feature in each cluster. This is due to the fact that each feature may have different importance in different clusters. The second advantage is using a specific kernelized distance to reduce the noise-sensitivity of the algorithm. The third one is using an analytical method for adapting feature weights during training process. Moreover, in this paper the mathematical analysis to proof the convergence of the algorithm has been presented. Several experiments were performed on an artificial dataset and also five real world datasets and the results show that the proposed method outperformed the other feature weighted clustering methods.