COMPARATIVE STUDY OF CLUSTERING TECHNIQUES IN MULTIVARIATE DATA ANALYSIS
Journal Title: International Journal of Engineering Sciences & Research Technology - Year 0, Vol 4, Issue 8
Abstract
In present, Clustering techniques is a standard tool in several exploratory pattern-analysis, grouping, decision making, and machine-learning situations; including data mining, document retrieval, image segmentation, pattern recognition and in the field of artificial intelligence. In this study we have compared five different types of clustering techniques such as Fuzzy clustering, K-Means clustering, Hierarchical Clustering, Principal Components Analysis (PCA) and Independent Component Analysis (ICA) based clustering to identify multivariate data clustering.
Authors and Affiliations
Sabba Ruhi,
Keywords
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