Object Oriented K-Means clustering using Eigen Decomposition for student data

Data clustering [1] is the process of forming classes or groups of similar data objects. The real time data objects are either multi-dimensional [4] or high dimensional [3]. Grouping these high dimensional data objects requires a lot of computational effort, time and space. To remove these hurdles f...

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Bibliographic Details
Published in:International journal of advanced research in computer science 2015-11, Vol.6 (8)
Main Authors: Nimmagadda, Sreeram, Kodati, Satya Prasad, Jonnalagadda, V R Murthy
Format: Article
Language:English
Subjects:
Computer science
Datasets
Decomposition
Eigenvalues
Eigenvectors
Hierarchies
Modularity
Principal components analysis
Online Access:Get full text
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Summary:Data clustering [1] is the process of forming classes or groups of similar data objects. The real time data objects are either multi-dimensional [4] or high dimensional [3]. Grouping these high dimensional data objects requires a lot of computational effort, time and space. To remove these hurdles from clustering of high dimensional data, the proposed work uses Eigen decomposition for dimensionality reduction, and then k-means is implemented through object oriented [2] programming on student's marks data. The experimental results show how Eigen value decomposition and object oriented implementation brings novelty to clustering process.
ISSN:0976-5697