Clustering of Yeast Gene Data Using WEKA

Abstract

In today’s world data mining have increasingly become very interesting and popular in terms of all application. The need for data mining is that we have too much data, too much technology but do not have useful information. This paper aims at clustering protein cellular localization of yeast genome. The knowledge of protein localization may provide valuable information in the target identification process for drug discovery and automated methods have become increasingly important in recent years owing to the steady increase in the amount of protein sequence data. Weka is a data mining tool and in this paper WEKA tool is used for clustering Yeast gene dataset obtained from UCI machine learning repository.