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Fault Prediction using Quad Tree and Expectation Maximization Algorithm

Meenakshi PC, Meenu S, Mithra M, Leela Rani P Published in

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
10.5120/ijais12-450338
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Meenakshi Pc, Meenu S, Mithra M and Leela Rani P. Article: Fault Prediction using Quad Tree and Expectation Maximization Algorithm. International Journal of Applied Information Systems 2(4):36-40, May 2012. Published by Foundation of Computer Science, New York, USA. BibTeX

@article{key:article,
	author = {Meenakshi Pc and Meenu S and Mithra M and Leela Rani P},
	title = {Article: Fault Prediction using Quad Tree and Expectation Maximization Algorithm},
	journal = {International Journal of Applied Information Systems},
	year = {2012},
	volume = {2},
	number = {4},
	pages = {36-40},
	month = {May},
	note = {Published by Foundation of Computer Science, New York, USA}
}

Abstract

The objective of the paper is to predict faults that tend to occur while classifying a dataset. There are various clustering algorithms that prevail to partition a dataset by some means of similarity. In this paper, a Quad Tree based Expectation Maximization (EM) algorithm has been applied for predicting faults in the classification of datasets. K-Means is a simple and popular approach that is widely used to cluster/classify data. However, K-Means does not always guarantee best clustering due to varied reasons. The proposed EM algorithm is known to be an appropriate optimization for finding compact clusters. EM guarantees elegant convergence. EM algorithm assigns an object to a cluster according to a weight representing the probability of membership. EM then iteratively rescores the objects and updates the estimates. The error-rate for K-Means algorithm and EM algorithm are computed, denoting the number of correctly and incorrectly classified samples by each algorithm. Result consists of charts showing on a comparative basis the effectiveness of EM algorithm with quad tree for fault prediction over the existing Quad Tree based K-Means (QDK) model.

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Keywords

Quad Tree, K-means Clustering, Expectation Maximization Algorithm, Iris Dataset, Clustering, Classification, Hyper-quad Tree