Publication Type:

Conference Paper

Source:

2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), IEEE, Chennai, India (2016)

ISBN:

9781509006120

URL:

https://ieeexplore.ieee.org/document/7919534

Keywords:

affinity matrix, Algorithm design and analysis, Clustering algorithms, Covariance, Covariance matrices, Data mining, data structural similarity, Data structures, Eigen values, Eigen vectors, eigenvalues, eigenvalues and eigenfunctions, eigenvectors, Gaussian kernel function, Gaussian processes, graph clustering method, graph construction, Graph theory, Image processing, K-means, K-Means clustering algorithm, Laplace equations, Laplacian, Laplacian graph matrices, Manifolds, Matrix algebra, normalized, pattern clustering, Pattern recognition, PCA, Principal component analysis, Similarity matrix, Similarity measure, spectral clustering, spectral graph, Stacking, un-normalized

Abstract:

In data mining, clustering is one of the most significant task, and has been widely used in pattern recognition and image processing. One of the tradition and most widely used clustering algorithm is k-Means clustering algorithm, but this algorithm fails to find structural similarity in the data or if the data is non-linear. Spectral clustering is a graph clustering method in which the nodes are clustered and useful if the data is non-linear and it finds clusters of different shapes. A spectral graph is constructed based on the affinity matrix or similarity matrix and the graph cut is found using Laplacian matrix. Traditional spectral clustering use Gaussian kernel function to construct a spectral graph. In this paper we implement PCA based similarity measure for graph construction and generated different Laplacian graphs for spectral clustering. In PCA based similarity measure, the similarity measure based on eigenvalues and its eigenvectors is used for building the graph and we study the efficiency of two types of Laplacian graph matrices. This graph is then clustered using spectral clustering algorithm. Effect of PCA similarity measure is analyzed on two types of Laplacian graphs i.e., un-normalized Laplacian and normalized Laplacian. The outcome shows accurate result of PCA measure on these two Laplacian graphs. It predicts perfect clustering of non-linear data. This spectral clustering is widely used in image processing.

Cite this Research Publication

Kavitha K. R., Sandeep, S., and Praveen, P. R., “Improved spectral clustering using PCA based similarity measure on different Laplacian graphs”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, India, 2016.