Abstract:
Biclustering is a commonly used technique for extracting local patterns from microarray data, for which several algorithms have been proposed. Hence it is important to de...Show MoreMetadata
Abstract:
Biclustering is a commonly used technique for extracting local patterns from microarray data, for which several algorithms have been proposed. Hence it is important to define metrics that compare the various algorithms. In this paper, we have defined novel measures of hausdorff distance between biclusters and global silhouette index for estimating the quality of biclusters extracted by the existing algorithms. We have also compared these measures with the standard measures such as the proportion of enriched biclusters for benchmark biological datasets. Our experimental results show almost similar variation of all these metrics for most of the datasets. The computation of these metrics for a given dataset for all the existing algorithms gives the most suited algorithm for the considered dataset.
Date of Conference: 09-12 November 2015
Date Added to IEEE Xplore: 17 December 2015
ISBN Information: