Similarity analysis of court judgments using clustering of case citation data: a study

  • Authors

    • Disna Davis Kachappilly Christ University
    • Rupali Sunil Wagh Christ University
    2018-06-01
    https://doi.org/10.14419/ijet.v7i2.9657
  • Citation Analysis, Data Clustering, Information Retrieval, Legal Domain, Similarity Search.
  • Information retrieval (IR) is an automatic mechanism to extract required information from a collection of unstructured or semi-structured data. IR systems minimize the effort of a user to locate the information based on the requirements. Clustering of documents is carried out as a preprocessing step for filtering irrelevant information in an IR system. Legal domain is a producer as well as consumer of huge in-formation which also contains invaluable legal knowledge and its interpretation. Knowledge based legal information retrieval systems is need of the day. Citation analysis is a technique to find the hidden relationships between the documents and is used for understanding knowledge transfer across various domains and hence becomes very important in legal domain. In this study, similarities among documents are analyzed using data clustering when applied on data of citations in court judgments.

  • References

    1. [1] Owen Byrd, Legal Analytics vs. Legal Research: What’s the Difference? Law Technology Today, 2017.

      [2] Julie Sobowale, How artificial intelligence is transforming the legal profession, ABA Journal, 2016.

      [3] Andrew Stranieri, John Zeleznikow, Tools for Intelligent Decision Support System Development in the legal domain, 2000.

      [4] Karl Branting, Data-centric and logic-based models for automated legal problem solving, Springer, 2017.

      [5] Sushanta Kumar, P. Krishna Reddy, V. Balakista Reddy, Malti Suri2, Finding Similar Legal Judgements under Common Law System, 2013.

      [6] Kayvan Kousha, Mike Thelwall, Patent Citation Analysis with Google, Association for Information Science and Technology, 2015.

      [7] Julie Callaert, Maikel Pellens, Bart Van Looy, Sources of Inspiration? Making Sense of Scientific References in Patents, 2014.

      [8] Anthony F.J. van Raan, Patent Citations Analysis and Its Value in Research Evaluation: A Review and a New Approach to Map Technology-relevant Research, Journal of Data and Information Science, Vol. 2, 2017.

      [9] Rupali Sunil Wagh, Knowledge Discovery from Legal Documents Dataset using Text Mining Techniques, International Journal of Computer Applications, Volume 66, 2013

      [10] Rupali Sunil Wagh, Exploratory Analysis of Legal Documents using Unsupervised Text Mining Techniquesâ€, International Journal of Engineering Research & Technology, Vol.3, 2014.

      [11] Nees Jan van Eck, Ludo Waltman, Citation-based clustering of publications using CitNetExplorer and VOSviewer, 2017.

      [12] Bader Aljaber, Nicola Stokes, James Bailey, Jian Pei, Document clustering of scientific texts using citation contexts, 2009.

      [13] Abdolreza Hatamlou, In search of optimal centroids on data clustering using a binary search algorithm, 2012.

      [14] Ming-Chuan Hung and Don-Lin Yang, an Efficient Fuzzy C-Means Clustering Algorithm, 2001.

      [15] J.L. Bentley, Multidimensional binary search trees used for associative searching, Communications of the ACM, Vol. 18(9), 1975, 509-517.

      [16] Kiri Wagstaf, Claire Cardie, Constrained K-means Clustering with Background Knowledge, Proceedings of the Eighteenth International Conference on Machine Learning, 2001,577–584.

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  • How to Cite

    Kachappilly, D. D., & Sunil Wagh, R. (2018). Similarity analysis of court judgments using clustering of case citation data: a study. International Journal of Engineering & Technology, 7(2), 855-858. https://doi.org/10.14419/ijet.v7i2.9657