Open AccessEnhanced Affinity for Spectral Clustering using Topological Node Features (TNFS)
Author(s) -
Lalith Srikanth Chintalapati,
Raghunatha Sarma Rachakonda
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a9450.109119
Subject(s) - cluster analysis , spectral clustering , mnist database , computer science , data mining , pattern recognition (psychology) , cure data clustering algorithm , correlation clustering , similarity (geometry) , node (physics) , metric (unit) , fuzzy clustering , artificial intelligence , measure (data warehouse) , pairwise comparison , single linkage clustering , physics , artificial neural network , engineering , operations management , quantum mechanics , image (mathematics)
Data clustering is an active topic of research as it has applications in various fields such as biology, management, statistics, pattern recognition, etc. Spectral Clustering (SC) has gained popularity in recent times due to its ability to handle complex data and ease of implementation. A crucial step in spectral clustering is the construction of the affinity matrix, which is based on a pairwise similarity measure. The varied characteristics of datasets affect the performance of a spectral clustering technique. In this paper, we have proposed an affinity measure based on Topological Node Features (TNFs) viz., Clustering Coefficient (CC) and Summation index (SI) to define the notion of density and local structure. It has been shown that these features improve the performance of SC in clustering the data. The experiments were conducted on synthetic datasets, UCI datasets, and the MNIST handwritten datasets. The results show that the proposed affinity metric outperforms several recent spectral clustering methods in terms of accuracy.