Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


March Edition 2023

International Journal of Applied Information Systems solicits high quality original research papers for the March 2023 Edition of the journal. The last date of research paper submission is February 15, 2023.

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
Download full text
  1. 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. BibTeX

    	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"


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.


  1. J. Han and M. Kamber, Data mining Concepts and techniques, 2nd edition, Morgan Kaufmann Publishers, pp. 401-404, 2007.
  2. http://archive. ics. uci. edu/ml/datasets/Iris
  3. M. Laszlo and S. Mukherjee, "A Genetic Algorithm Using Hyper-Quad trees for Low-Dimensional K-Means Clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no4, pp. 533-543, 2006.
  4. http://en. wikipedia. org/wiki/Cluster_analysis
  5. http://infolab. stanford. edu/~ullman/fcdb/oracle/or-jdbc. html#0. 1_create
  6. Osama Abu Abbas, Computer Science Department, Yarmulke University, Jordan, "Comparisons between data clustering algorithms" The international Arab Journal of Information Technology,Vol. 5,No. 3,July 2008.
  7. P. S. Bishnu and V. Bhattacharjee, "Software Fault prediction using Quad tree based K-Means method," IEEE transactions on Knowledge and Data Engineering ,Vol. PP, No. 99, May 2011
  8. P. S. Bishnu and V. Bhattacherjee, "Outlier Detection Technique Using Quad Tree," Proc of Int. conf. on Computer Communication Control and Information Technology, pp. 143-148, Feb 2009.
  9. T. Kanungo, D. M. Mount, N. Netanyahu, C. Piatko, R. Silverman and A. Y. Wu, "An Efficient K-Means clustering Algorithm: Analysis and Implementation", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, pp. 881-892, 2002.
  10. P. S. Bishnu and V. Bhattacherjee, "A New Initialization method for K-Means using Quad Tree," Proc of National. conf. on Methods and Models in Computing, JNU, New Delhi, pp. 73-81, 2008.
  11. R. A. Finkel and J. L. Bentley, Quad Trees: a Data Structure for Retrieval on Composite key. Acta information, vol. 4, no. 1, pp. 1-9, 1974


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