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A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays

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  • Leila M Naeni
  • Hugh Craig
  • Regina Berretta
  • Pablo Moscato

Abstract

In this study we propose a novel, unsupervised clustering methodology for analyzing large datasets. This new, efficient methodology converts the general clustering problem into the community detection problem in graph by using the Jensen-Shannon distance, a dissimilarity measure originating in Information Theory. Moreover, we use graph theoretic concepts for the generation and analysis of proximity graphs. Our methodology is based on a newly proposed memetic algorithm (iMA-Net) for discovering clusters of data elements by maximizing the modularity function in proximity graphs of literary works. To test the effectiveness of this general methodology, we apply it to a text corpus dataset, which contains frequencies of approximately 55,114 unique words across all 168 written in the Shakespearean era (16th and 17th centuries), to analyze and detect clusters of similar plays. Experimental results and comparison with state-of-the-art clustering methods demonstrate the remarkable performance of our new method for identifying high quality clusters which reflect the commonalities in the literary style of the plays.

Suggested Citation

  • Leila M Naeni & Hugh Craig & Regina Berretta & Pablo Moscato, 2016. "A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-27, August.
  • Handle: RePEc:plo:pone00:0157988
    DOI: 10.1371/journal.pone.0157988
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    References listed on IDEAS

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    1. Ferdinand Österreicher & Igor Vajda, 2003. "A new class of metric divergences on probability spaces and its applicability in statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(3), pages 639-653, September.
    2. Mario Inostroza-Ponta & Regina Berretta & Pablo Moscato, 2011. "QAPgrid: A Two Level QAP-Based Approach for Large-Scale Data Analysis and Visualization," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-18, January.
    3. Pablo M. Gleiser & Leon Danon, 2003. "Community Structure In Jazz," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 565-573.
    4. Shang, Ronghua & Bai, Jing & Jiao, Licheng & Jin, Chao, 2013. "Community detection based on modularity and an improved genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1215-1231.
    5. Natalie Jane de Vries & Jamie Carlson & Pablo Moscato, 2014. "A Data-Driven Approach to Reverse Engineering Customer Engagement Models: Towards Functional Constructs," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-19, July.
    6. Julien Jacques & Cristian Preda, 2014. "Functional data clustering: a survey," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 231-255, September.
    7. Hathaway, Richard J. & Bezdek, James C., 2006. "Extending fuzzy and probabilistic clustering to very large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 215-234, November.
    8. Duncan J. Watts, 2007. "A twenty-first century science," Nature, Nature, vol. 445(7127), pages 489-489, February.
    9. Liu, Jian & Liu, Tingzhan, 2010. "Detecting community structure in complex networks using simulated annealing with k-means algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(11), pages 2300-2309.
    10. Ahmed Shamsul Arefin & Luke Mathieson & Daniel Johnstone & Regina Berretta & Pablo Moscato, 2012. "Unveiling Clusters of RNA Transcript Pairs Associated with Markers of Alzheimer’s Disease Progression," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-25, September.
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    1. Mayra Z Rodriguez & Cesar H Comin & Dalcimar Casanova & Odemir M Bruno & Diego R Amancio & Luciano da F Costa & Francisco A Rodrigues, 2019. "Clustering algorithms: A comparative approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-34, January.

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