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S. Rajathi

Bio: S. Rajathi is an academic researcher from Sathyabama University. The author has contributed to research in topics: Canopy clustering algorithm & Affinity propagation. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Papers
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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


Cited by
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Proceedings ArticleDOI
01 Jun 2018
TL;DR: A novel method is proposed to achieve a high recognition rate in many real-life surveillance zones such as banks, airports and corridors, based on dividing a gait cycle into several phases using Constrained Fuzzy C-Means method and converging feature information of a stream into one feature descriptor using gait Cycle analysis.
Abstract: Gait as a significant biometric feature in human identification is drawing a wide attention nowadays. In many real-life surveillance zones such as banks, airports and corridors, gait recognition is often restricted from the front view. There are situations where a complete gait cycle is not always available due to frame drop caused by devices and the limitation in space of such areas, while most of the existing methods require at least one complete gait cycle. A novel method is proposed to achieve a high recognition rate in such application scenarios, based on dividing a gait cycle into several phases using Constrained Fuzzy C-Means method and converging feature information of a stream into one feature descriptor using gait cycle analysis. Experimental results demonstrate the high performance of our method comparing to other existing ones.

4 citations