Data Analysis of Bio-Medical Data Mining using Enhanced Hierarchical Agglomerative Clustering
R Venkata Krishnaiah, Chandra Sekar · 2012
Data mining became increasingly important in bioinformatics and biomedical area during last decade. Various data mining methods, like clustering, have successfully revealed previous unknown knowledge in bioinformatics and biomedical area. Hierarchical Clustering is one of the wide areas of knowledge analysis, within the categorical data mining domain. In several contexts and domains, hierarchical agglomerative clustering (HAC) offers best-quality results, but at the price of a high complexity which reduces the size of datasets which can be handled. In some contexts, in particular, computing distances between objects is the most expensive task. In this paper we propose an approach called Enhanced Hierarchical Clustering Approach (EHAC), aimed at improving performance, reliability of data, which is well integrated in all the phases of the Entropy based Mean Clustering Approaches and can be applied to single-linkage HAC Process. After describing the method, we provide some theoretical evidence of its pruning power, followed by an empirical study of its effectiveness over different data domains, with a special focus on dimensionality issues.