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S. Revathy

Bio: S. Revathy is an academic researcher. The author has contributed to research in topics: k-medians clustering & Fuzzy classification. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper proposes new modified rough fuzzy clustering algorithm based on fuzzy rough correlation factor, which can be derived directly from the results obtained thro fuzzy clustered.
Abstract: There are advantages to both fuzzy set and rough set theories, Combining these two and used for clustering gives better results. Rough clustering is less restrictive than hard clustering and less descriptive than fuzzy clustering. Rough clustering is an appropriate method since it separates the objects that are definite members of a cluster from the objects that are only possible members of a cluster. In fuzzy clustering similarities are described by membership degrees while in rough clustering definite and possible members to a cluster are detected. Fuzzy Rough Correlation Factor is the threshold for degree of fuzziness. It determines how low a DFR value shall be for it to be considered for cluster membership assignment. This paper proposes new modified rough fuzzy clustering algorithm based on fuzzy rough correlation factor. Hence rough fuzzy clustering can be derived directly from the results obtained thro fuzzy clustering.

3 citations


Cited by
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Journal ArticleDOI
TL;DR: Decision theory is propagated, an unprecedented validation scheme for Rough-Fuzzy clustering by resolving loss and probability calculations to predict the risk measure in clustering techniques, proven to deduce the optimal number of clusters overcoming the downsides of traditional validation frameworks.
Abstract: Cluster validation is an essential technique in all cluster applications. Several validation methods measure the accuracy of cluster structure. Typical methods are geometric, where only distance and membership form the core of validation. Yao's decision theory is a novel approach for cluster validation, which evolved loss calculations and probabilistic based measure for determining the cluster quality. Conventional rough set algorithms have utilized this validity measure. This paper propagates decision theory, an unprecedented validation scheme for Rough-Fuzzy clustering by resolving loss and probability calculations to predict the risk measure in clustering techniques. Experiments with synthetic and UCI datasets have been performed, proven to deduce the optimal number of clusters overcoming the downsides of traditional validation frameworks. The proposed index can also be applied to other clustering algorithms and extends the usefulness in business oriented data mining.

2 citations

Proceedings ArticleDOI
01 Dec 2013
TL;DR: Calculating cluster validity indices for a synthetic dataset and a real dataset on applying these algorithms and ensuring best soft clustering algorithm through experimental analysis are reviewed.
Abstract: Clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups Clustering can be considered one of the most important unsupervised learning techniques so as every other problem of this kind; it deals with finding a structure in a collection of unlabelled data Clustering is of soft and hard clustering Hard clustering refers to basic partitioning algorithms where object belongs to only one cluster Soft clustering refers to data objects belonging to more than one cluster based on its membership values This paper reviews three types of Soft clustering techniques: Fuzzy C-Mean, Rough C-Mean, and Rough Fuzzy C-Mean Thereby calculating cluster validity indices for a synthetic dataset and a real dataset on applying these algorithms and ensuring best soft clustering algorithm through experimental analysis

2 citations

Proceedings ArticleDOI
08 Sep 2014
TL;DR: A feature based sentiment classification method that helps a user to make decisions easily based on their features of interest is proposed.
Abstract: The era of social networking has lead to the availability of vast amount of information in the web. People express their opinion about product, services or public issues in the forums, review sites, blogs etc. But in order to get useful data it becomes necessary to apply NLP techniques which make it easy for the people to make decisions at the time of buying products or contracting services. All the users are not concerned with all features of a product. Hence this paper proposes a feature based sentiment classification method that helps a user to make decisions easily based on their features of interest.

1 citations