General Rough Modeling of Cluster Analysis

A Rough Cluster Analysis of Shopping Orientation Data

This paper describes the application of a new technique, rough clustering, to the problem of market segmentation. Rough clustering produces different solutions to k-means analysis because of the possibility of multiple cluster membership of objects. Traditional clustering methods generate extensional descriptions of groups, that show which objects are members of each cluster. Clustering techniq...

Cluster Analysis Using Rough Clustering and k-Means Clustering

IntroductIon Cluster analysis is a fundamental data reduction technique used in the physical and social sciences. It is of potential interest to managers in Information Science, as it can be used to identify user needs though segmenting users such as Web site visitors. In addition, the theory of Rough sets is the subject of intense interest in computational intelligence research. The extension ...

Rough Set Theory: Approach for Similarity Measure in Cluster Analysis

Clustering of data is an important data mining application. One of the problems with traditional partitioning clustering methods is that they partition the data into hard bound number of clusters. Rough set based Indiscernibility relation combined with indiscernibility graph, leads to knowledge discovery in an elegant way. Indiscernibilty relation has a strong appeal to be applied in clustering...

Cluster-Based Rough Set Construction

In many data mining applications, cluster analysis is widely used and its results are expected to be interpretable, comprehensible, and usable. Rough set theory is one of the techniques to induce decision rules and manage inconsistent and incomplete information. This paper proposes a method to construct equivalence classes during the clustering process, isolate outlier points and finally deduce...

Modeling Rough Surfaces