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Hybrid visual computing models to discover the clusters assessment of high dimensional big data


Clusters assessment is a major identified problem in big data clustering. Top big data partitioning techniques, such as, spherical k-means, Mini-batch-k-means are widely used in many large data applications. However, they need prior information about the clusters assessment to discover the quality of clusters over the big data. Existing visual models, namely, clustering with improved visual assessment of tendency, and sample viewpoints cosine-based similarity VAT (SVPCS-VAT), efficiently perform the clusters assessment of big data. For the high-dimensional big data, the SVPCS-VAT is enhanced with the subspace learning techniques, principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), Neighborhood preserving embedding (NPE). These are used to develop hybrid visual computing models, including PCA-based SVPCS-VAT, LDA-based SVPCS-VAT, and LPP-based SVPCS-VAT, NPE-based SVPCS-VAT to overcome the curse of dimensionality problem. Experimental is conducted on benchmarked datasets to demonstrate and compare the efficiency with the state-of-the-art big data clustering methods.

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Enquiries about data availability should be directed to the authors.


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Authors and Affiliations



MSB and SKM contributed toward designing hybrid visual computing models. MSB has collected the related study data of visual techniques for clusters assessment problems. KRP carried out data analysis and interpretation of clustering results analysis with indicate measures. He performed the critical investigations of the work in the experimental. MSB wrote the paper with the advice of other authors, and SM took the revision for the quality of the paper. MSB, SKM, KRP: Conceptualization; MSB, SKM: Data curation; MSB, SKM, KRP: Formal analysis; KRP: Funding acquisition, Funding—“Science and Engineering Research Board (SERB)” – Grant of DST (Department of Science and Technology), Government of India, Sanctioned File Number-ECR/2016/001556 MSB, SKM, KRP: Investigation; MSB, SKM, KRP: Three New Methods are developed they are, PCA-based SVPCS-VAT, LDA-based SVPCS-VAT, and LPP-based SVPCS-VAT; SKM: Project administration; MSB, RP: Resources; SK, KRP: Supervision; SB: Visualization; MSB, KRP: Writing—original draft; SKM: Writing—review and editing.

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Correspondence to M. Suleman Basha.

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Suleman Basha, M., Mouleeswaran, S.K. & Rajendra Prasad, K. Hybrid visual computing models to discover the clusters assessment of high dimensional big data. Soft Comput 27, 4249–4262 (2023).

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  • Data clustering
  • Cluster tendency
  • Visual models
  • Big data
  • Subspace learning