A Data Mining Technique for Tourist Destination Brand Image Building
Rahul Kaul1, Manmohan Singh2, Sweta Gupta3, Geetanjli Khambra4, Prashant Pathak5

1Mr. Rahul Kaul, Assistant Professor, Department of Computer Science & Engineering, CDGI, Indore, India.
2Dr. Manmohan Singh, Professor, Department of Computer Science and Engineering, CDGI, Indore, India.
3Mrs. Sweta Gupta, Assistant Professor, Department of School of Engineering &Technology, Jagran Lakecity University, Bhopal, India.
4Ms. Geetanjli Khambra Raghuwanshi, Assistant Professor, Department of Computer Applications, BSSS, Bhopal (M.P), India.
5Mr. Prashant Pathak, Assistant Professor, Department of Computer Science, Tilak Maharashtra Vidyapeeth, Pune (Maharashtra), India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4617-4622 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8329038620/2020©BEIESP | DOI: 10.35940/ijrte.F8329.038620

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© The Authors. 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: The destination image branding is the domain of tourism industry where the facts and information is collected and evaluated for finding the credibility of a target tourist destination. Manual collection and processing of collected information accurately is a complicated and time consuming task therefore a data mining model is suggested ,in this presented work that collect and evaluate the destination image accurately and based on evaluation can make the recommendations about visits of tourist. In order to perform this task data mining techniques are applied on text data source. In first the data is extracted from the Google search engine and it is preprocessed for make it impure. In further the data is labeled based on the positive and negative words available in the collected facts. Finally the clustering and classification of text is performed. For clustering of data FCM (fuzzy c means) clustering algorithm and for classification the Bayesian classifier is used. Based on final classification of text data the decision is made for the destination visits.
Keywords: Brand Building, Destination Image Building, Data Mining Techniques, Clustering.
Scope of the Article: Energy Efficient Building Technology.