When no prior knowledge is available, clustering is a useful technique for categorizing data
into meaningful groups or clusters. In this paper, a modified fuzzy min–max (MFMM) …

A hybrid intelligent model comprising a modified fuzzy min-max (FMM) clustering neural
network and a modified clustering tree (CT) is developed. A review of clustering models with …

Fuzzy clustering has emerged as an important tool for discovering the structure of data.
Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy …

In this paper, a modified fuzzy min–max neural network (MFMC) for data clustering is
proposed. In MFMC, the centroid information, the similarity and the noise of data are taken …

The efficiency frontier analysis has been an important approach of evaluating firms'
performance in private and public sectors. There have been many efficiency frontier analysis …

This paper presents the Fuzzy C-Means (FCM) clustering method. The FCM technique
assigns a degree of membership for each data set to several clusters, thus offering the …

This study proposes a weighted incremental fuzzy C-mean power load clustering algorithm
based on the dynamic conditional score model to solve the problems that the predominant …

One of the standard approaches for data analysis in unsupervised machine learning
techniques is cluster analysis or clustering, where the data possessing similar features are …

In the electricity market, it is highly desirable for suppliers to know the electricity consumption
behavior of their customers, in order to provide them with satisfactory services with the …

In the electricity market, it is highly desirable for suppliers to know the electricity consumption
behavior of their customers, in order to provide them with satisfactory services with the …