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kClusterHub: An AutoML-Driven Tool for Effortless Partition-Based Clustering over Varied Data Types

Author

Listed:
  • Konstantinos Gratsos

    (Department of Information and Electronic Engineering, School of Engineering, International Hellenic University, Sindos, 57400 Thessaloniki, Greece)

  • Stefanos Ougiaroglou

    (Department of Information and Electronic Engineering, School of Engineering, International Hellenic University, Sindos, 57400 Thessaloniki, Greece)

  • Dionisis Margaris

    (Department of Digital Systems, School of Economics and Technology, University of the Peloponnese, 23100 Sparta, Greece)

Abstract

Partition-based clustering is widely applied over diverse domains. Researchers and practitioners from various scientific disciplines engage with partition-based algorithms relying on specialized software or programming libraries. Addressing the need to bridge the knowledge gap associated with these tools, this paper introduces kClusterHub, an AutoML-driven web tool that simplifies the execution of partition-based clustering over numerical, categorical and mixed data types, while facilitating the identification of the optimal number of clusters, using the elbow method. Through automatic feature analysis, kClusterHub selects the most appropriate algorithm from the trio of k-means, k-modes, and k-prototypes. By empowering users to seamlessly upload datasets and select features, kClusterHub selects the algorithm, provides the elbow graph, recommends the optimal number of clusters, executes clustering, and presents the cluster assignment, through tabular representations and exploratory plots. Therefore, kClusterHub reduces the need for specialized software and programming skills, making clustering more accessible to non-experts. For further enhancing its utility, kClusterHub integrates a REST API to support the programmatic execution of cluster analysis. The paper concludes with an evaluation of kClusterHub’s usability via the System Usability Scale and CPU performance experiments. The results emerge that kClusterHub is a streamlined, efficient and user-friendly AutoML-inspired tool for cluster analysis.

Suggested Citation

  • Konstantinos Gratsos & Stefanos Ougiaroglou & Dionisis Margaris, 2023. "kClusterHub: An AutoML-Driven Tool for Effortless Partition-Based Clustering over Varied Data Types," Future Internet, MDPI, vol. 15(10), pages 1-22, October.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:10:p:341-:d:1262259
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    References listed on IDEAS

    as
    1. Ziqi Jia & Ling Song, 2020. "Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, July.
    2. Brock, Guy & Pihur, Vasyl & Datta, Susmita & Datta, Somnath, 2008. "clValid: An R Package for Cluster Validation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i04).
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