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A Hybrid Ant–Fuzzy Approach for Data Clustering in a Distributed Environment

  • K. Sumangala
  • S. Sathappan
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

Mining the relevant documents from distributed databases is a challenging task in the era of ‘big data’. This paper presents a hybrid approach using two different metaheuristics algorithms inspired by nature, namely, the enhanced ant clustering algorithm (EACA) and the fuzzy clustering algorithm, which deal with the uncertainty in data mining. The first algorithm uses the ant colony metaphor, which is one of the most recent nature-inspired metaheuristics, and the second employs the fuzzy clustering approach. The proposed work aims to introduce the fuzzy approach to the ant-based algorithm for both local and global levels, that is, interclustering and interzone clustering, in mining distributed databases, and a new hybrid (ant–fuzzy) algorithm is developed for data clustering under a distributed environment. The proposed cluster-based framework presents the important concepts of web mining and its various real-time applications. The real-time synthetic data and, also, the training datasets from the UCI Machine Learning Repository were employed to evaluate the performance of the algorithms. The performance of the proposed work is compared with the EACA, probabilistic ant-based clustering (PACE), and K-means algorithms in terms of accuracy with the F-measure and the error rate with entropy measure.

Keywords

Ant clustering EACA PACE Fuzzy C-means Distributed clustering 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • K. Sumangala
    • 1
  • S. Sathappan
    • 2
  1. 1.Kongunadu Arts and Science CollegeCoimbatoreIndia
  2. 2.Erode Arts and Science CollegeErodeIndia

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