Ranking and dimensionality reduction using biclustering

Abstract

Organizing and searching the data tries to detect groups where objects exhibit similar properties. As the dimensionality d increases, the space in which data is represented increases rapidly therefore the available data becomes sparse. When d is high, all objects appear to be sparse and dissimilar in many ways. Here, a study is made to reduce the number of dimensions using biclustering method to rank the features/dimensions. Classification rate is used as validation criteria for the selection of appropriate dimensions. Ranking algorithms such as Relief F, Symmetrical uncertainty and Information gain are compared with the proposed ranking using biclustering. It is found that for large data sets with large number of dimensions, ranking using biclustering achieves classification rates with less number of features than the other ranking algorithms.