EDSC: Efficient document subspace clustering technique for high-dimensional data

Abstract

Abstract-With the advancement in the pervasive technology, there is a spontaneous rise in the size of the data. Such data are generated from various forms of resources right from individual to organization level. Due to the characteristics of unstructured or semi-structuredness in data representation, the existing data analytics approaches are not directly applicable which leads to curse of dimensionality problem. Hence, this paper presents an Efficient Document Subspace Clustering (EDSC) technique for highdimensional data that contributes to the existing system with respect to identification by eliminating the redundant data. The discrete segmentation of data points a r e u s e d to explicitly expose the dimensionality of hidden subspaces in the clusters. The outcome of the proposed system was compared with existing system to find the effective document clustering process for high-dimensional data. The processing time of EDSC for subspace clustering is reduced by 50% as compared to the existing system.

RMSC: Robust modeling of subspace clustering for high dimensional data

“…Our earlier review work [10] has already discussed about various techniques and approaches towards similar issues as well as highlighted some of the open research issues. We have also presented a sub-clustering technique over document form in high-dimensional data [11].…”

Framework for novel subspace clustering using search optimization methodology

“…Therefore, the problem statement of the proposed study can be stated as "Designing a cost-effective methodology for performing subspace clustering in the presence of critical issues and errorprone environment poses to degrade data quality in highdimensional data." b) Proposed Solution The proposed system is in continuation of our prior research work [29] [30]. The present research work emphasizes on improving the accuracy factor while performing subspace clustering for highdimensional data.…”