Identifying tourist route patterns using data mining techniques

In this study, researchers applied data mining techniques to reveal tourist route patterns to popular destinations in Surat Thani Province in southern Thailand. Data mining refers to the process of discovering patterns in large data.Two data mining techniques were employed: 1) Cluster analysis was used to identify unique clusters of tourists with common behavioral trends. 2) Association rule mining was used to determine tourist route patterns.From these two data mining techniques, the researchers were able to identify unique clusters of tourists who followed common patterns of travel.The main implications of this study are: 1) that data mining may be used to explain the movement of tourists in any region in the world, and 2) that different facets of the tourism industry can use this information to understand and respond to tourists' needs and interests

Classifying Words: A Syllables-Based Model

Abstract—Text classification has been extensively studied by linguists and computer scientists. However, there are very few works on classification of words into classes or concepts (e.g. thesaurus). In this paper, we consider this topic, especially in the context of the classification of names like brand names or neologisms. The challenge is thus to provide automated tools to analyze new names by classifying them into concepts. Then, for example, a naming company customer can be informed about which concept a new name is closest to. As we argue that a word can belong to several concepts, we propose to consider the top-k classification approach. Moreover, we rely on syllables to build the classification model. The word corpus is collected from French thesaurus. All labeled-words are separated into syllables. Feature selection techniques are used to select discriminative syllables. We use a syllables frequency (SF) and mutual information (MI) performing with Naive Bayes classifier and K-nearest neighbor (KNN). Instead of selecting only one class, the model select top-k classes ranking them by a classifier score. The result shows the top-k classification model helps to analyze a new word by showing that it can be related to more than one concept. Moreover, the set of discriminative syllables can be used to explain the classification results which makes the results more meaningful