
A Data Mining Technique for Tourist Destination Brand Image Building
Author(s) -
Mr. Rahul Kaul*,
Dr.Manmohan Singh,
Mrs Sweta Gupta,
Ms. Geetanjli khambra Raghuwanshi,
Mr. Prashant Pathak
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8329.038620
Subject(s) - cluster analysis , computer science , tourism , data mining , credibility , classifier (uml) , task (project management) , fuzzy clustering , information retrieval , image (mathematics) , artificial intelligence , geography , engineering , archaeology , systems engineering , political science , law
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.