Research

iVAT and aVAT: Enhanced Visual Analysis for Cluster Tendency Assessment


Reference:

Wang, L., Nguyen, U. T. V., Bezdek, J. C., Leckie, C. A. and Ramamohanarao, K., 2010. iVAT and aVAT: Enhanced Visual Analysis for Cluster Tendency Assessment. In: Zaki, M. J., Yu, J. X., Ravindran, B. and Pudi, V., eds. Advances in Knowledge Discovery and Data Mining, Pt I, Proceedings. Vol. 6118. Springer, pp. 16-27. (Lecture Notes in Artificial Intelligence)

Related documents:

This repository does not currently have the full-text of this item.
You may be able to access a copy if URLs are provided below.

Official URL:

http://dx.doi.org/10.1007/978-3-642-13657-3_5

Abstract

Given a pairwise dissimilarity matrix D of a set of n objects, visual methods (such as VAT) for cluster tendency assessment generally represent D as an n x n image I((D) over tilde) where the objects are reordered to reveal hidden cluster structure as dark blocks along the diagonal of the image. A major limitation of such methods is the inability to high-light cluster structure in I((D) over tilde) when D contains highly complex clusters. To address this problem, this paper proposes an improved VAT (iVAT) method by combining a path-based distance transform with VAT. In addition, an automated VAT (aVAT) method is also proposed to automatically determine the number of clusters from I((D) over tilde). Experimental results on several synthetic and real-world data sets have demonstrated the effectiveness of our methods.

Details

Item Type Book Sections
CreatorsWang, L., Nguyen, U. T. V., Bezdek, J. C., Leckie, C. A. and Ramamohanarao, K.
EditorsZaki, M. J., Yu, J. X., Ravindran, B. and Pudi, V.
DOI10.1007/978-3-642-13657-3_5
DepartmentsFaculty of Science > Computer Science
StatusPublished
ID Code20989
Additional Information14th Pacific-Asia Conference on Knowledge Discovery and Data Mining. 21-24 June 2010. Hyderabad, India.

Export

Actions (login required)

View Item