A New Implementation of the co-VAT Algorithm for Visual Assessment of Clusters in Rectangular Relational Data

This paper presents a new implementation of the co-VAT algorithm. We assume we have an m × n matrix D, where the elements of D are pair-wise dissimilarities between m row objects Or and n column objects Oc. The union of these disjoint sets are (N = m + n) objects O. Clustering tendency assessment is the process by which a data set is analyzed to determine the number(s) of clusters present. In 2007, the co-Visual Assessment of Tendency (co-VAT) algorithm was proposed for rectangular data such as these. co-VAT is a visual approach that addresses four clustering tendency questions: i) How many clusters are in the row objects Or? ii) How many clusters are in the column objects Oc? iii) How many clusters are in the union of the row and column objects Or ∪ Oc? And, iv) How many (co)-clusters are there that contain at least one of each type? co-VAT first imputes pair-wise dissimilarity values among the row objects, the square relational matrix Dr, and the column objects, the square relati...

• Type: Conference
• Year: 2010
• Where: ICAISC
• Authors: Timothy C. Havens, James C. Bezdek, James M. Keller
• Keywords: Artificial Intelligence, Column Objects, Column Objects Oc, ICAISC 2010, Row Objects