Using biclustering for automatic attribute selection to enhance global visualization

Data mining involves useful knowledge discovery using a data matrix consisting of records and attributes or variables. Not all the attributes may be useful in knowledge discovery, as some of them may be redundant, irrelevant, noisy or even opposing. Furthermore, using all the attributes increases the complexity of solving the problem. The Minimum Attribute Subset Selection Problem (MASSP) has been studied for well over three decades and researchers have come up with several solutions In this paper a new technique is proposed for the MASSP based on the crossing minimization paradigm from the domain of graph drawing using biclustering. Biclustering is used to quickly identify those attributes that are significant in the data matrix. The attributes identified are then used to perform one-way clustering and generate pixelized visualization of the clustered results. Using the proposed technique on two real datasets has shown promising results.