An Integrated Framework for Mixed Data Clustering Using Growing Hierarchical Self-Organizing Map (GHSOM)

• Published in: Mathematical Modelling and Scientific Computation pp. 471 - 479
• Main Authors: Hari Prasad, D., Punithavalli, M.
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg
• Series: Communications in Computer and Information Science
• Subjects: Attribute-Oriented Induction; Growing Hierarchical Self-Organizing Map (GHSOM); Pattern Discovery; Self-Organizing Map (SOM)
• DOI: 10.1007/978-3-642-28926-2_53
• ISBN: 3642289258; 9783642289255
• ISSN: 1865-0929

Clustering plays an important role in data mining of large data and helps in analysis. This develops a vast importance in research field for providing better clustering technique. There are several techniques exists for clustering the similar kind of data. But only very few techniques exists for clustering mixed data items. The cluster must be such that the similarity of items within the clusters is increased and the similarity of items from different clusters must be reduced. The existing techniques possess several disadvantages. To overcome those drawbacks, Self-Organizing Map (SOM) and Extended Attribute-Oriented Induction (EAOI) for clustering mixed data type data can be used. This will take more time for clustering; the usage of SOM has the inability to capture the inherent hierarchical structure of data. To overcome this, a Growing Hierarchical Self-Organizing Map (GHSOM) is proposed in this paper. The experimentation is done by using UCI Adult Data Set.