Object Oriented K-Means clustering using Eigen Decomposition for Student Data

N.Sree Ram, Dr.K.Satya Prasad, Dr.J.V.R.Murthy


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.


Keywords: Eigen decomposition; k-means; object oriented implementation; clustering; high dimensional data

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DOI: https://doi.org/10.26483/ijarcs.v6i8.2589


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