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.
|Name||Lecture Notes in Artificial Intelligence|
|Conference||14th Pacific-Asia Conference on Knowledge Discovery and Data Mining|
|Abbreviated title||PAKDD 2010|
|Period||21/06/10 → 24/06/10|