Using Ant Colony Optimization to Build Cluster-Based Classification Systems

  • Khalid M. Salama
  • Ashraf M. AbdelbarEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9882)


Learning cluster-based classification systems is the process of partitioning a training set into data subsets (clusters), and then building a local classifier for each data cluster. The class of a new instance is predicted by first assigning the instance to its nearest cluster, and then using that cluster’s local classification model to predict the instance’s class. In this paper, we use the Ant Colony Optimization (ACO) meta-heuristic to optimize the data clusters based on a given classification algorithm in an integrated cluster-with-learn manner. The proposed ACO algorithms use two different clustering solution representation approaches: instance-based and medoid-based, where in the latter the number of clusters is optimized as part of the ACO algorithm’s execution. In our experiments, we employ three widely-used classification algorithms, k-nearest neighbours, Ripper, and C4.5, and evaluate performance on 30 UCI benchmark datasets. We compare the ACO results to the traditional c-means clustering algorithm, where the data clusters are built prior to learning the local classifiers.


Ant Colony Optimization (ACO) Data mining Classification Clustering Cluster-based classification system 



Partial support of a grant from the Brandon University Research Council is gratefully acknowledged.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of ComputingUniversity of KentCanterburyUK
  2. 2.Department of Mathematics & Computer ScienceBrandon UniversityManitobaCanada

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