Fuzzy Rough Set Approaches For Data Clustering
Usama Mokhtar Hassen Hasaneen;
Abstract
One of the most important methods in analysis of large data sets is clustering. Cluster analysis is a technique for classifying data, i.e., to divide a given dataset into a set of classes or clusters. The goal is to divide the dataset in such a way that two cases from the same cluster are as similar as possible and two cases from different clusters are as dissimilar as possible.
In this thesis, we propose modification to one of modem clustering which is Fuzzy Rough C-Means (FRCM). The algorithm named at Merging Fuzzy Rough C-Means (MFRCM) Clustering Algorithm. This algorithm can achieve high accuracy result when it compare with other trademark clustering algorithm such FCM and FRCM it self. Aim of This algorithm to cluster data with a minimum number of errors in clustering process.
In this thesis, we propose modification to one of modem clustering which is Fuzzy Rough C-Means (FRCM). The algorithm named at Merging Fuzzy Rough C-Means (MFRCM) Clustering Algorithm. This algorithm can achieve high accuracy result when it compare with other trademark clustering algorithm such FCM and FRCM it self. Aim of This algorithm to cluster data with a minimum number of errors in clustering process.
Other data
| Title | Fuzzy Rough Set Approaches For Data Clustering | Other Titles | اساليب تقسيم البيانات باستخدام اساليب التعامل مع البيانات المبهمه | Authors | Usama Mokhtar Hassen Hasaneen | Issue Date | 2009 |
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