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A Comparative Study of Classification and Clustering Methods from Text of Books

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Intelligent Information and Database Systems (ACIIDS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13758))

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Abstract

Book collections in libraries are an important means of information, but without proper assignment of books into appropriate categories, searching for books on similar topics is very troublesome for both librarians and readers. This is a difficult problem due to the analysis of large sets of real text data, such as the content of books. For this purpose, we propose to create an appropriate model system, the use of which will allow for automatic assignment of books to appropriate categories by analyzing the text from the content of the books. Our research was tested on a database consisting of 552 documents. Each document contains the full content of the book. All books are from Project Gutenberg in the Art, Biology, Mathematics, Philosophy, or Technology category. Well-known techniques of natural language processing (NLP) were used for the proper preprocessing of the book content and for data analysis. Then, two different machine learning approaches were used: classification (as supervised learning) and clustering (as unsupervised learning) in order to properly assign books to selected categories. Measures of accuracy, precision and recall were used to evaluate the quality of classification. In our research, good classification results were obtained, even above 90% accuracy. Also, the use of clustering algorithms allowed for effective assignment of books to categories.

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Notes

  1. 1.

    https://www.gutenberg.org/.

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Authors and Affiliations

  1. Department of Machine Learning, University of Economics in Katowice, 1 Maja, 40-287, Katowice, Poland

    Barbara Probierz, Jan Kozak & Anita Hrabia

Authors
  1. Barbara Probierz
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  2. Jan Kozak
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  3. Anita Hrabia
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Corresponding author

Correspondence to Barbara Probierz .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tien Khoa Tran

  3. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ualsher Tukayev

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  6. University of Newcastle, Newcastle, NSW, Australia

    Edward Szczerbicki

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Probierz, B., Kozak, J., Hrabia, A. (2022). A Comparative Study of Classification and Clustering Methods from Text of Books. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_2

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  • DOI: https://doi.org/10.1007/978-3-031-21967-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21966-5

  • Online ISBN: 978-3-031-21967-2

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