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Document Clustering Using Hybrid LDA- Kmeans

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Applied Informatics and Cybernetics in Intelligent Systems (CSOC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1226))

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Abstract

This paper presents a Hybrid Latent Dirichlet Allocation – Kmeans (HLDA-Kmeans) Algorithm for document clustering. The overload information has became a challenge for users due to the existence of abundance information and heterogeneous nature of the Web. Researchers such as academician as well as people who are involved in text analytics have encountered challenges to analyze documents because of ambiguity in keywords/keyphrases. Hence, the objective is to perform document clustering analysis using HLDA - Kmeans algorithm to discover the clusters among the unlabelled text data, classify the keyphrases based on topics and visualize the clustering results. Online news from Oil and Gas is used as a dataset for training and testing using 70%–30% split. The system performance of the proposed HLDA - Kmeans algorithm was assessed using Precision, Recall and F-Score Formulas. Experimental results show that the proposed HLDA - Kmeans has achieved clustering results satisfactorily.

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Acknowledgement

The authors would like to thank YUTP (Cost Center 015LC0-173) for the financial support in the research.

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Authors and Affiliations

  1. Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Perak, Malaysia

    Oi-Mean Foong & Alia Nabila Ismail

Authors
  1. Oi-Mean Foong
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  2. Alia Nabila Ismail
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Corresponding author

Correspondence to Oi-Mean Foong .

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Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

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Foong, OM., Ismail, A.N. (2020). Document Clustering Using Hybrid LDA- Kmeans. In: Silhavy, R. (eds) Applied Informatics and Cybernetics in Intelligent Systems. CSOC 2020. Advances in Intelligent Systems and Computing, vol 1226. Springer, Cham. https://doi.org/10.1007/978-3-030-51974-2_12

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