Binary data clustering based on Wiener transformation

Clustering is the process of grouping similar items. Clustering becomes very tedious when data dimensionality and sparsity increases. Binary data are the simplest form of data used in information systems for very large database and it is very efficient based on computational efficiency, memory capacity to represent categorical type data. Usually the binary data clustering is done by using 0 and 1 as numerical value. In this paper, the binary data clustering is performed by preprocessing the binary data to real by wiener transformation. Wiener is a linear Transformation based upon statistics and it is optimal in terms of Mean square error. Computational results show that the clustering based on Wiener transformation is very efficient in terms of objectivity and subjectivity.