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

J Anuradha, B K Tripathy
2014 International Journal of Intelligent Systems and Applications  
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. M isdiagnosis 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
more » ... 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. Index Terms-Hierarchical Clustering, Attribute Dependency, ADHD, Cluster Purity. I. World Scientific, Springer and Science Direct publications. Also, he is in the editorial board of at least 8 international journals. His current research interest includes Fuzzy sets and systems, Rough sets and knowledge engineering, Granular computing, soft computing, bag theory, list theory and social network analysis. How to cite this paper:
doi:10.5815/ijisa.2014.06.04 fatcat:wfqsryvygzf4paodpek55xuodq