International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.6, No.6, May. 2014

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

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J Anuradha, B K Tripathy

Index Terms

Hierarchical Clustering, Attribute Dependency, ADHD, Cluster Purity


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.

Cite This Paper

J Anuradha, B K Tripathy,"Hierarchical Clustering Algorithm based on Attribute Dependency for Attention Deficit Hyperactive Disorder", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.6, pp.37-45, 2014. DOI: 10.5815/ijisa.2014.06.04


[1]Poushaneh K, Bonab B.G. and Namin F.H., Effect of training impulse control on increase attention of children with attention-deficit/hyperactivity disorder, Ptocedia Social and Behavioral Science, vol. 5, 2010, pp 983 - 987.

[2]Tan T.S, Cheung W.S, Effects of computer collaborative group work on peer acceptance of a junior pupil with Attention Deficit Hyperactive Disorder, Computer and Education, 2008, vol.50, pp 725 – 741.

[3]Seal S, Komarina S and Aluru S, An optimal hierarchical clustering algorithm for gene expression data, Information Processing Letters, vol. 93, 2005, pp 143-147.

[4]Grub, M. and Laryn, L.: Behavioral Characteristics of DSM-IV ADHD subtypes in a school – Based population, Journal of abnormal child Psychology, vol. 25, No. 2, 1997, pp 103 – 111.

[5]Lopez V, Lopez-Alderon J, Ortega R, Kreither R, Carrasco X, Rothhammer P, Rothhammer F, Rosas R and Aboitiz F, Attention-deficit hyperactive disorder involves differerential cortical processing in a visual spatial attention paradigm, Clinical Neurophysiology, vol. 117, 2006, pp 2540 - 2548.

[6]Han J., Kamber, M. and Pei J: Data Mining: Concepts and Techniques, 3rd Edition, Morgan Kaufmann Publishers, 2012.

[7]Yu L, Gao L, Li K, Zhao Y and Chiu D.K.Y, A degree-distribution based hierarchical agglomerative clustering algorithm for protein complexes identification, Computational Biology and Chemistry, vol. 35, 2011, pp 298 - 307.

[8]Lee John W.T, Yeung D.S, Tasng E.C.C: Hierarchical clustering based on ordinal consistency, Pattern Recognition, vol. 38, 2005, pp 1913-1925.

[9]Wu J, Xiong H and Chen J, Towards understanding hierarchical clustering: A data distribution perspective, Neuro Computing, vol. 72, 2009, pp 2319 - 2330.

[10]Vijaya P.A, Narasimha Murty and Subramanian, Leaders-Subleaders: An efficient hierarchical clustering algorithm for large data sets, Pattern Recognition Letters, vol. 25, 2004, pp 505-513.

[11]Qiao S, Li Q, Li H, Peng J and Chen H, A new block modeling based hierarchical clustering algorithm for web social networks, Engineering Applications of Artificial Intelligence, vol. 25, 2012, pp 640 - 647.

[12]Tu Q, Lu J.F, Yuan B, Tang J.B and Yang J.Y, Density based Hierarchical Clustering for streaming data, Pattern Recognition Letters, vol. 33, 2012, pp 641 - 645.

[13]Vijaya P.A, Narasimha Murty and Subramanian D.K, Efficient bottom-up hybrid hierarchical clustering techniques for protein sequence classification, Pattern Recognition, vol. 39, 2006, pp 2344 - 2355.

[14]Ananthanarayana V.S., Narasimha Murty and M., Subramanian, D.K.: Efficient clustering of large data sets, Pattern Recognition, vol.34, 2001, pp 2561-2563.

[15]Zimek A, Thesis on Correlation clustering, University of Munchen, 2008.

[16]Bhattacharya A and De, Rajat K.: Average correlation clustering algorithm (ACCA) for grouping co-regulated genes with similar pattern of variation in their expression values, Journal of Biomedical Informatics, vol.43, 2010, pp560-568. 

[17]Shji Hirano, Tsumoto S, Okuzari T, Hata Y, Clustering based on Rough set and Applications to knowledge Discovery in Medical Diagnosis, MEDINFO, 2001, pp 206-210.