Authors : L.V. Arun Shalin and K. Prasadh
Abstract: Clustering intends to divide the subset and group the similar objects with respect to the given similarity measure. Clustering includes number of techniques which includes statistics, pattern recognition, data mining and other fields. Projected clustering screens the data set into numerous disjoint clusters with the outliers so that each cluster exists in a subspace. The majority of the clustering techniques are considerably incompetent in providing the objective function. To overcome the attribute relevancy and the redundancy by providing the objective function, we are going to implement a new technique termed Multi cluster Dimensional Projection on Quantum Distribution (MDPQD). This technique evolved a discrete dimensional projection clusters using the Quantum Distribution Model. Multi cluster dimension on quantum distribution considers the problem of relevancy of attribute and redundancy. An analytical and empirical result offers a multi cluster formation based on objective function and to evolve a dimensional projection clusters. Performance of the multi cluster dimensional projection on quantum distribution is measured in terms of an efficient multi cluster formation with respect to the data set dimensionality, comparison of accuracy with all other algorithms and scalability of quantum distribution.
L.V. Arun Shalin and K. Prasadh, 2014. Multi Cluster Dimensional Projection on Quantum Distributed Interference Data. Asian Journal of Information Technology, 13: 599-605.