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Subspace Clustering for High-Dimensional Data Using Cluster Structure Similarity

Author

Listed:
  • Kavan Fatehi

    (Yazd University, Department of Computer Engineering, Yazd, Islamic Republic of Iran)

  • Mohsen Rezvani

    (Shahrood University of Technology, Department of Computer Engineering, Shahrood, Islamic Republic of Iran)

  • Mansoor Fateh

    (Shahrood University of Technology, Department of Computer Engineering, Shahrood, Islamic Republic of Iran)

  • Mohammad-Reza Pajoohan

    (Yazd University, Department of Computer Engineering, Yazd, Islamic Republic of Iran)

Abstract
This article describes how recently, because of the curse of dimensionality in high dimensional data, a significant amount of research has been conducted on subspace clustering aiming at discovering clusters embedded in any possible attributes combination. The main goal of subspace clustering algorithms is to find all clusters in all subspaces. Previous studies have mostly been generating redundant subspace clusters, leading to clustering accuracy loss and also increasing the running time of the algorithms. A bottom-up density-based approach is suggested in this article, in which the cluster structure serves as a similarity measure to generate the optimal subspaces which result in raising the accuracy of the subspace clustering. Based on this idea, the algorithm discovers similar subspaces by considering similarity in their cluster structure, then combines them and the data in the new subspaces would be clustered again. Finally, the algorithm determines all the subspaces and also finds all clusters within them. Experiments on various synthetic and real datasets show that the results of the proposed approach are significantly better in quality and runtime than the state-of-the-art on clustering high-dimensional data.

Suggested Citation

  • Kavan Fatehi & Mohsen Rezvani & Mansoor Fateh & Mohammad-Reza Pajoohan, 2018. "Subspace Clustering for High-Dimensional Data Using Cluster Structure Similarity," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 14(3), pages 38-55, July.
    • Handle: RePEc:igg:jiit00:v:14:y:2018:i:3:p:38-55
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    Cited by:

    1. Lifeng Mu & Vijayan Sugumaran & Fangyuan Wang, 2020. "A Hybrid Genetic Algorithm for Software Architecture Re-Modularization," Information Systems Frontiers, Springer, vol. 22(5), pages 1133-1161, October.
    2. Lifeng Mu & Vijayan Sugumaran & Fangyuan Wang, 0. "A Hybrid Genetic Algorithm for Software Architecture Re-Modularization," Information Systems Frontiers, Springer, vol. 0, pages 1-29.

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