Authors: P. Prabhavathy; B.K. Tripathy
Addresses: School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu, India ' School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India
Abstract: Clustering is categorised as hard or soft in nature. Soft clusters may have fuzzy or rough boundaries. Rough clustering can help researchers to discover overlapping clusters in many applications such as web mining and text mining. Rough set approach is a very useful tool to handle the unclear and ambiguous data. As rough sets make use of the equivalence relation property, they remain rigid and it is unreliable and inefficient for real time applications where the datasets may be very large. In this paper, we provide a solution to this problem with covering-based rough set approach. Covering-based rough set is an extension of rough set approach in which the equivalence relation has been relaxed. This method is based on coverings rather than partitions. This makes it more flexible than rough sets and it is more convenient for dealing with complex applications. Clustering sequential data is one of the vital research tasks. We uses covering-based similarity measure which gives better results as compared to rough set which uses set and sequence similarity measure. In this paper, covering-based rough fuzzy set clustering approach is proposed to resolve the uncertainty of sequence data.
Keywords: rough sets; fuzzy sets; covering; clustering; sequential data; similarity measures; uncertainty.
International Journal of Reasoning-based Intelligent Systems, 2015 Vol.7 No.3/4, pp.296 - 304
Available online: 09 Nov 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article