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. © 2015 Inderscience Enterprises Ltd.

Prabhavathy P

Department of Information Technology

School of Information Technology and Engineering

Vellore Campus

Balakrushna Tripathy

Department of Analytics

School of Computer Science and Engineering

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

International Journal of Reasoning-based Intelligent Systems

In this paper, we have implemented a unique form of clustering that takes a non-numeric data set and clusters it with the help of the word embedding provided by the GloVe dataset. The related word embedding are generated for each of the items in the dataset we want to cluster using the GloVe vector representation of those words. We then perform dimensionality reduction on the data set to obtain the accurate number of dimensions to be taken for appropriate cluster formation. The data is then clustered using k-means++. This paper provides one of the ways to overcome the limitation of k-means clustering in terms of initialising the cluster centres and hence gives better quality clusters. With the synthetic examples, the k-means method does not perform well, because the random seeding inevitably merges clusters together, and the algorithm is unable to then split them apart. Careful seeding method used by k-means++ prevents this problem and hence usually gives optimal results even when datasets are synthetic. © 2017 IEEE.

2017 International Conference on Intelligent Sustainable Systems (ICISS)

Implementing GloVe for context based k-means++ clustering

Rough sets introduced by Pawlak as a model to capture impreciseness in data is unigranular from the granular computing point of view. In their attempts to introduce multigranulation using rough sets, two models called optimistic multigranular rough sets and pessimistic multigranular rough sets were introduce by Qian et al. in 2006 and 2010 respectively. It is well known that there are several extensions of the basic rough sets to make it more applicable and covering based rough set approach is one among them. Recently, the concept of multigranularity has been extended by Liu et al. to introduce four types of covering based optimistic multigranular rough sets (CBOMGRS). Later, Tripathy et al. introduced the notion of Covering Based Pessimistic Multigranular Rough Sets (CBPMGRS) and proved some of their properties. The important observation is that some of the properties of basic rough sets, which were not true for CBOMGRS, are true for CBPMGRS. So, CBPMGRS seems to be more natural extension of the basic concept than CBOMGRS. Recently Chai has shown that many of the results established by Liu et al. are incorrect, which they rectified. In our results in this paper, we analyse the same for CBPMGRS and incorporate the changes and establish some more properties of CBPMGRS. © Springer India 2015.

Advances in Intelligent Systems and Computing Information Systems Design and Intelligent Applications

Some More Properties of Covering Based Pessimistic Multigranular Rough Sets

Rough sets were introduced by Pawlak as a model to capture impreciseness in data and several techniques in different directions have been developed to perform operations on such data through the model. But the basic model of rough set introduced is unigranular from the granular computing point of view. So, attempts have been made to introduce multigranulation using rough sets. Two models called optimistic multigranular rough sets and pessimistic multigranular rough sets were introduce by Qian et al in 2006 and 2010 respectively. However, like the basic case, the information granules generated were introduced by equivalence relations and as a result the granules are members of partitions induced on the universe. However, such concepts have limited applications due to the rarity of equivalence relations or equivalently partitions of universes the notion of cover is more general than partition and covers are available in abundance in real life situations. So, even in the case of basic rough sets the definition has been extended to develop covering based rough sets. Recently, the concept of multigranularity has been extended by Liu et al to introduce four types of covering based optimistic multigranular rough sets (CBOMGRS). In this paper we introduce the notion of Covering Based Pessimistic Multigranular Rough Sets (CBPMGRS) and proved some properties the important observation is that some of the properties of basic rough sets, which were not true for CBOMGRS, are true for CBPMGRS. So, CBPMGRS seems to be more natural extensions of the basic concepts than CBOMGRS. © 2014 IEEE.

2014 International Conference on Computational Intelligence and Communication Networks

On Covering Based Pessimistic Multigranular Rough Sets

International Journal of Granular Computing, Rough Sets and Intelligent Systems

Some topological properties of rough sets and their applications

Approximations of classifications, introduced and studied by Grzymala Busse [4] is a notion different from the notion of approximations of sets (Pawlak [9]). In fact the equivalence classes of approximate classifications cannot be arbitrary sets. Busse [4] had established properties of approximations of classifications which were recently extended to necessary and sufficient type theorems by Tripathy et. al ([12]). Four types of covering based rough sets have been obtained as generalization of basic rough sets. Also another covering rough set from a topological point of view has been obtained (14, 15, 16, 17, 18, 19]. Attempts have been made in [13] to extend the above results to covering based rough sets. In this paper we carry out this study further by deriving results for all the five types of covering based rough sets. These results can be used to derive rules for information systems with domains of attributes values being covers instead of partitions. Copyright © 2009 ACM.

Proceedings of the 2nd International Conference on Interaction Sciences Information Technology, Culture and Human - ICIS '09

On some properties of covering based approximations of classifications of sets

Covering based rough sets have been introduced as an extension of the basic rough sets introduced by Pawlak [5, 6]. There is only one way to define the lower approximation of a set using covering. However, as many as four definitions have been proposed for covering based upper approximation. Accordingly, we find four types of covering based rough sets in the literature. In [7], the concept of rough equivalences of sets, which extended the corresponding notions of approximate equalities of Novotny and Pawlak [2, 3, 4] were introduced and their properties were studied. In this article we further generalize these concepts to the setting of covering based rough sets and establish some of their properties. © 2009 IEEE.

Journal	International Journal of Reasoning-based Intelligent Systems
Publisher	Inderscience Publishers
ISSN	1755-0556
Open Access	0