Optimisation of sub-space clustering in a high dimension data using Laplacian graph and machine learning
by P.R. Ambika; A. Bharathi Malakreddy
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 18, No. 1/2, 2022

Abstract: There are many applications like business analytics, computer vision and medical data analytics, where an unsupervised approach of learning is used for the high-dimension data (HDD) clustering. The problem of the subspace clustering is modelled as a graph problem which has to retain the critical features from the N-dimension while applying a dimension reduction technique to maintain a higher accuracy and lower computational overhead trade-off. Most of the traditional approaches suffer from the efficiency degradation when applied to HDD. An optimisation of sub-space clustering is proposed in this paper for learning models using Laplacian graph on a HDD. The proposed model addresses the curse of dimensionality problem through Laplacian matrix function to minimise the data redundancy within sub-space. The traditional K-nearest neighbour (KNN) algorithm is improvised for the non-linear classification of subspace clustering on HDD clinical importance. The proposed system offers significant increment of 99% of accuracy in clustering operation.

Online publication date: Thu, 07-Apr-2022

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