Pattern Recognition and Stylometry Analysis of Pathittrupathu in Tamil Literature
Manimannan G.1, M. Salomi2, R. Lakshmi Priya3
1Manimannan G*., Assistant Professor, Department of Statistics, TMG College of Arts and Science, Chennai, (Tamil Nadu), India.
2M. Salomi, Assistant Professor, Department of Statistics, Madras Christian College, Chennai, (Tamil Nadu), India.
3R. Lakshmi Priya, Assistant Professor, Department of Statistics, Dr. Ambedkar Government Arts College, Vyasarpadi, Chennai, (Tamil Nadu), India.
Manuscript received on September 30, 2020. | Revised Manuscript received on October 10, 2020. | Manuscript published on October 15, 2020. | PP: 10-15 | Volume-5 Issue-2, October 2020. | Retrieval Number: 100.1/ijmh.B1143105220 | DOI: 10.35940/ijmh.B1143.105220
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This research paper attempts to identify the structure and clustering of Sangam Literature Pathittrpathu in Tamil using Data Mining Techniques. Pathittrupathu means, it is split into ten tens poems, which is a poetic work and one of the eight anthologies available in ancientTamil literature. This work originally contains ten divisions of ten poems each dedicated tovarious Chera kings rule of Tamil, except the poetic works of first and the last ten poems have been lost in before sangam period of Tamil. The remain eight poetic works is considered as a database in this research paper. Twenty parameters were used to identify the structure and pattern of Sangam literature of Tamil. Initially, usage of words is identifiedusing descriptive statistics and pattern of poetic structure is traced by using hierarchical data mining techniques. The result shows that all the eight Pathittrupathu split into two cluster and their writing styles were unique. The clustering methods achieved same clusters and named as natural clusters.
Keywords: Samgam Literature, Pathittrupathu, Stylometry Analysis, Data Mining, Descriptive Statistics and Cluster Analysis.