P.G. Sunitha Hiremath

Work place: Dept. of Information Science and Engg., BVB College of Engineering and Tech., Hubli, Karnataka, India

E-mail: pgshiremath64@gmail.com


Research Interests: Computer systems and computational processes, Computer Architecture and Organization, Computer Networks, Data Mining, Data Structures and Algorithms


Ms. P. G. Sunitha Hiremath is working as an Associate Professor, Dept. of Information Science & Engineering, B V Bhoomaraddi College of Engineering & Technology, Hubli, Karnataka, India. She received B.E. degree in Electronics and Communication Engg. from Gulbarga University, Karnataka, India. M.S. degree in Software Systems from BITS, Pilani, Rajasthan and M.Tech. in Computer Network Engg., VTU, Belgaum. She is pursuing Ph.D programme in Computer Networks from JNTU, Hyderabad. Her research interest includes Data Mining, Hybrid mobile adhoc networks and Data Analytics. She published 8 research papers in peer reviewed International Journals and Conferences.

Author Articles
Application of Data Mining in the Classification of Historical Monument Places

By Siddu P. Algur Prashant Bhat P.G. Sunitha Hiremath

DOI: https://doi.org/10.5815/ijisa.2016.08.07, Pub. Date: 8 Aug. 2016

The economic development and promotion of a country or region is depends on several facts such as- tourism, industries, transport, technology, GDP etc. The Government of the country is responsible to facilitate the opportunities to develop tourism, technology, transport etc. In view of this, we look into the Department of Tourism to predict and classify the number of tourists visiting historical Indian monuments such as Taj- Mahal, Agra, and Ajanta etc.. The data set is obtained from the Indian Tourist Statistics which contains year wise statistics of visitors to historical monuments places. A survey undertaken every year by the government is preprocessed to fill out the possible missing values, and normalize inconsistent data. Various classification techniques under Decision Tree approach such as- Random Tree, REPTree, Random Forest and J48 algorithms are applied to classify the historical monuments places. Performance evaluation measures of the classification models are analyzed and compared as a step in the process of knowledge discovery.

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