Advertisement

Nearest Cluster Classifier

  • Hamid Parvin
  • Moslem Mohamadi
  • Sajad Parvin
  • Zahra Rezaei
  • Behrouz Minaei
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7208)

Abstract

In this paper, a new classification method that uses a clustering method to reduce the train set of K-Nearest Neighbor (KNN) classifier and also in order to enhance its performance is proposed. The proposed method is called Nearest Cluster Classifier (NCC). Inspiring the traditional K-NN algorithm, the main idea is to classify a test sample according to the tag of its nearest neighbor. First, the train set is clustered into a number of partitions. By obtaining a number of partitions employing several runnings of a simple clustering algorithm, NCC algorithm extracts a large number of clusters out of the partitions. Then, the label of each cluster center produced in the previous step is determined employing the majority vote mechanism between the class labels of the patterns in the cluster. The NCC algorithm iteratively adds a cluster to a pool of the selected clusters that are considered as the train set of the final 1-NN classifier as long as the 1-NN classifier performance over a set of patterns included the train set and the validation set improves. The selected set of the most accurate clusters are considered as the train set of final 1-NN classifier. After that, the class label of a new test sample is determined according to the class label of the nearest cluster center. Computationally, the NCC is about K times faster than KNN. The proposed method is evaluated on some real datasets from UCI repository. Empirical studies show an excellent improvement in terms of both accuracy and time complexity in comparison with KNN classifier.

Keywords

Nearest Cluster Classifier K-Nearest Neighbor Combinational Classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fix, E., Hodges, J.L.: Discriminatory analysis, nonparametric discrimination: Consistency properties. Technical Report 4, USAF School of Aviation Medicine, Randolph Field, Texas (1951)Google Scholar
  2. 2.
    Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inform. Theory IT-13(1), 21–27 (1967)Google Scholar
  3. 3.
    Hellman, M.E.: The nearest neighbor classification rule with a reject option. IEEE Trans. Syst. Man Cybern. 3, 179–185 (1970)Google Scholar
  4. 4.
    Fukunaga, K., Hostetler, L.: k-nearest-neighbor bayes-risk estimation. IEEE Trans. Information Theory 21(3), 285–293 (1975)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Dudani, S.A.: The distance-weighted k-nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. SMC-6, 325–327 (1976)Google Scholar
  6. 6.
    Bailey, T., Jain, A.: A note on distance-weighted k-nearest neighbor rules. IEEE Trans. Systems, Man, Cybernetics 8, 311–313 (1978)zbMATHCrossRefGoogle Scholar
  7. 7.
    Bermejo, S., Cabestany, J.: Adaptive soft k-nearest-neighbour classifiers. Pattern Recognition 33, 1999–2005 (2000)zbMATHCrossRefGoogle Scholar
  8. 8.
    Jozwik, A.: A learning scheme for a fuzzy k-nn rule. Pattern Recognition Letters 1, 287–289 (1983)CrossRefGoogle Scholar
  9. 9.
    Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nn neighbor algorithm. IEEE Trans. Syst. Man Cybern. SMC-15(4), 580–585 (1985)Google Scholar
  10. 10.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons (2000)Google Scholar
  11. 11.
    Itqon, S.K., Satoru, I.: Improving Performance of k-Nearest Neighbor Classifier by Test Features. Springer Transactions of the Institute of Electronics, Information and Communication Engineers (2001)Google Scholar
  12. 12.
    Lam, L., Suen, C.Y.: Application of majority voting to pattern recognition: An analysis of its behavior and performance. IEEE Transactions on Systems, Man, and Cybernetics 27(5), 553–568 (1997)CrossRefGoogle Scholar
  13. 13.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)zbMATHGoogle Scholar
  14. 14.
    Newman, C.B.D.J., Hettich, S., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLSummary.html
  15. 15.
    Wu, X.: Top 10 algorithms in data mining. In: Knowledge Information, pp. 22–24. Springer-Verlag London Limited (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hamid Parvin
    • 1
  • Moslem Mohamadi
    • 1
  • Sajad Parvin
    • 1
  • Zahra Rezaei
    • 1
  • Behrouz Minaei
    • 1
  1. 1.Nourabad Mamasani BranchIslamic Azad UniversityNourabad MamasaniIran

Personalised recommendations