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

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.