Knowledge Discovery for Tourism Using Data Mining and Qualitative Analysis: A Case Study at Johor Bahru, Malaysia

Knowledge Discovery for Tourism Using Data Mining and Qualitative Analysis: A Case Study at Johor Bahru, Malaysia

Atae Rezaei Aghdam (Department of Information Systems, Universiti Teknologi Malaysia, Johor Bahru, Malaysia), Mostafa Kamalpour (Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia) and Alex Tze Hiang Sim (Department of Information System, Universiti Teknologi Malaysia, Johor Bahru, Malaysia)
DOI: 10.4018/ijabim.2014100105
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This paper aims to propose a new guideline for analyzing tourist profiles as found in These have been examined from two different aspects so as to gain conclusive results. Tourist data were “crawled” from through a specific web crawler. Mining techniques using a combination of visualization, clustering, and association rules were instrumental in discovering the first set of interesting knowledge. This was followed by a qualitative analysis applied through Nvivo software via coding of the tourist's comments in order to define the design of the prospective model. A final set of results was obtained once both results confirmed each other. In this study, results show that there are several types of tourists; with each group having different preferences. For example: male Singaporean visitors to hotels tend to enjoy wine and food in addition to outdoor activities; while local visitors to Legoland are not satisfied with certain aspects, such as the price of food. International tourists, however, consider the affirmative points of Legoland. This research can be very useful for tourist associations and hotel managers in Johor Bahru.
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2. Literature Review

Currently, data mining supports various kinds of application tasks: from data pre-processing to association rules discovery, data classification, and cluster analysis respectively (Witten & Frank, 2011). Actually, it is part of the decision-making process; and the availability to analyze data automatically helps to determine a potential model. It also assists in estimating customer behavior in the realm of enhancing decision makers’ ability to both adjust marketing strategy and reduce risks (Li, 2012; Han & Kamber 2006). In another study (Bose, 2009), concentration was given to three main aspects of using data mining in the tourism industry. These are, namely; forecasting tourist expenditure, analyzing profiles of tourists and forecasting the number of tourist arrivals. The author has found various results based on these three dimensions. For instance, in forecasting tourist expenditure, artificial intelligence sources such as Neural Network were used for estimating tourist expenditure in the Balearic Islands. Further, Au & Law (2002) used data mining techniques to predict shopping expenditure by tourists with an accuracy level of 94%. In relation to analyzing tourist profiles (Bose, 2009), categorized tourists into specific groups such as; developmental support, prudent developers, ambivalent, cautious and protectionist respectively by using a clustering technique. In forecasting tourist arrivals, some studies had been conducted examining tourist arrivals to Hong Kong from six different countries; Artificial Neural Network (ANN) showed that this outweighed statistical methods. In a further study, Bose (2009) stated that it has been argued that, to date, only some AI techniques such as ANN and clustering techniques have been used in tourism data mining. It is largely prepackaged software that uses these techniques readily; it can also be used with little training to analyze data. However, the author believed that, in the future, more than one method will be applied for analyzing data. In this paper we focus on analyzing tourist profiles from two different perspectives, namely: quantitative and qualitative. In the following sections, the related studies illustrate how traditional data mining techniques have been used in the tourism industry.

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