Rough fuzzy hybrid models are widely used for handling uncertain and vague data and are very efficient in handling real life applications. Particle swarm optimisation (PSO) has been found to be a useful tool to optimise and find the best out of a set of solutions. In this paper, we propose a computational algorithm by embedding PSO in rough fuzzy hybrid clustering, which forms overlapping clusters with optimised partition. The proposed algorithm uses rough fuzzy C-means to formulate fuzzy lower and fuzzy boundary region of the clusters based on membership of objects with respect to their prototypes. This method has been applied to a swarm of clusters to get the best partitions at local and global levels qualified by Davies Bouldin (DB) and Dunn (D) indexes as fitness measures. This algorithm generates clusters dynamically and its superiority over other existing clustering techniques is established experimentally by taking several real world datasets. Copyright © Inderscience Enterprises Ltd.

Balakrushna Tripathy

Department of Analytics

School of Computer Science and Engineering

Vellore Campus

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 Data Mining, Modelling and Management

Cluster Computing

A framework for smart traffic management using hybrid clustering techniques

Attention Deficit Hyperactivity Disorder (ADHD) is a Disruptive Behaviour Disorder characterized by the presence of a set of chronic and impairing behaviour patterns that display abnormal levels of inattention, hyperactivity, or their combination. Since most individuals especially children display these behaviours from time to time, it is be difficult to differentiate behaviours that reflect ADHD from those that are a normal part of growing up which makes the diagnosis a tricky job. In this paper, we apply a well known artificial intelligence technique, the SVM algorithm, for the diagnosis of the disorder. The major advantage of using SVM is that it helps in controlling the complexity of the problem of diagnosing. There has not been much development or research on ADHD using SVM algorithm. Hence this is the first attempt at diagnosing the problems using the algorithm. To improve on the overall identification accuracy; we also make use of the GA-based, Feature Selection Algorithm. Genetic algorithms are known to give good solution to very complex problems. In conclusion, we expect that AI techniques like SVM will certainly play an essential role in future ADHD diagnosis applications. © 2010 ACM.

Proceedings of the Third Annual ACM Bangalore Conference on - COMPUTE '10

Diagnosis of ADHD using SVM algorithm

Methods of Multivariate Analysis Wiley Series in Probability and Statistics

Classification Analysis: Allocation of Observations to Groups

Expert Systems with Applications

Fuzzy C-means and fuzzy swarm for fuzzy clustering problem

Data & Knowledge Engineering

The incremental method for fast computing the rough fuzzy approximations

Pattern Recognition

Shadowed c-means: Integrating fuzzy and rough clustering

An optimal rough fuzzy clustering algorithm using particle swarm optimisation

Journal	International Journal of Data Mining, Modelling and Management
Publisher	Inderscience Publishers
ISSN	1759-1163
Open Access	0