Analyse Power Consumption by Mobile Applications Using Fuzzy Clustering Approach

A Multi-Objective Approach to Fuzzy Clustering using ITLBO Algorithm

Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering data can measurably increase the quality of clustering. In this study, a model with two ...

OPTIMIZATION OF FUZZY CLUSTERING CRITERIA BY A HYBRID PSO AND FUZZY C-MEANS CLUSTERING ALGORITHM

This paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzyclustering problem, especially for large sizes. When the problem becomes large, theFCM algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. The PSO algorithm does find ago...

Energy Consumption Optimization using Clustering in Mobile Ad-Hoc Network

In this In MANET (Mobile Ad hoc Network) nodes are not fixed on one position that’s why proper communication is very critical issue. The nodes in MANET depend on battery for communication. Therefore energy efficiency is an important design deliberation to extend the lifetime of networks. The cluster based approach provides the efficient communication in the form of group. The nodes in Mobile ad...

New Approach for Customer Clustering by Integrating the LRFM Model and Fuzzy Inference System

This study aimed at providing a systematic method to analyze the characteristics of customers’ purchasing behavior in order to improve the performance of customer relationship management system. For this purpose, the improved model of LRFM (including Length, Recency, Frequency, and Monetary indices) was utilized which is now a more common model than the basic RFM model apt for analyzing the cus...

Fuzzy clustering models and applications

Image Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach

Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-...