Objective-Based Rough c-Means Clustering

Abstract

Conventional clustering algorithms classify a set of objects into some clusters with clear boundaries, that is, one object must belong to one cluster. However, many objects belong to more than one cluster in real world since the boundaries of clusters generally overlap with each other. Fuzzy‐set representation of clusters makes it possible for each object to belong to more than one cluster. On the other hand, the fuzzy degree is sometimes regarded as too descriptive for interpreting clustering results. Rough‐set representation could deal with such cases. Clustering based on rough set could provide a solution that is less restrictive than conventional clustering and less descriptive than fuzzy clustering. This paper proposes a rough clustering algorithm which is based on optimization of an objective function and the calculation formula of cluster centers is the same as one by Lingras et al. Moreover, it shows effectiveness of our proposed clustering algorithm in comparison with other algorithms.

Large-Scale Time Series Clustering Based on Fuzzy Granulation and Collaboration

“…The objective of clustering is to partition some objects into several clusters such that similar objects fall into the same cluster as far as possible. In the past few decades, a large number of clustering algorithms emerged such as model‐based clustering method, hierarchical clustering method, and objective function‐based clustering method …”