J Anuradha

Work place: School of Computing Science and Engineering, VIT University, Vellore, Tamilnadu, India

E-mail: januradha@vit.ac.in


Research Interests: Medical Informatics, Autonomic Computing, Computer Architecture and Organization, Data Mining, Data Structures and Algorithms


J Anuradha is an Assistant professor in the school of computing sciences and engineering, VIT University, at Vellore, India. She has more than 8 years of teaching experience and is currently doing her PhD in computational Intelligence. She has published technical papers in international journals/ proceedings of international conferences/ edited book chapters of reputed publications. Her current research interest includes Fuzzy sets and systems, Rough sets and knowledge engineering, Data Mining, soft computing, and Medical Diagnosis.

Author Articles
Hierarchical Clustering Algorithm based on Attribute Dependency for Attention Deficit Hyperactive Disorder

By J Anuradha B.K. Tripathy

DOI: https://doi.org/10.5815/ijisa.2014.06.04, Pub. Date: 8 May 2014

Attention Deficit Hyperactive Disorder (ADHD) is a disruptive neurobehavioral disorder characterized by abnormal behavioral patterns in attention, perusing activity, acting impulsively and combined types. It is predominant among school going children and it is tricky to differentiate between an active and an ADHD child. Misdiagnosis and undiagnosed cases are very common. Behavior patterns are identified by the mentors in the academic environment who lack skills in screening those kids. Hence an unsupervised learning algorithm can cluster the behavioral patterns of children at school for diagnosis of ADHD. In this paper, we propose a hierarchical clustering algorithm to partition the dataset based on attribute dependency (HCAD). HCAD forms clusters of data based on the high dependent attributes and their equivalence relation. It is capable of handling large volumes of data with reasonably faster clustering than most of the existing algorithms. It can work on both labeled and unlabelled data sets. Experimental results reveal that this algorithm has higher accuracy in comparison to other algorithms. HCAD achieves 97% of cluster purity in diagnosing ADHD. Empirical analysis of application of HCAD on different data sets from UCI repository is provided.

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