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Tourist Destination Recommendation System based on Machine Learning
(2024-10-03)
Kongpeng, Sumitra
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Hanskunatai, Anantaporn
Thailand has a wide variety of tourist attractions, making it difficult for tourist to choose where to go on vacation. The tourist destination recommendation system is a challenge for creating a system to help recommend tourist destinations that are appropriate for personal. Therefore, the principal aims of this research encompass two distinct objectives: firstly, to create a recommendation system for tourist destinations in Thailand by applying machine learning algorithms; and secondly, to analyze factors influencing tourists' choices of destinations. The dataset was gathered from an online survey conducted via Google Forms, comprising responses from 429 tourists in Thailand. In the experiments, three different types of feature selection methods were applied in a data preprocessing step. In the modeling process, four machine learning algorithms, namely Decision Tree, Random Forest, k-Nearest Neighbors (k-NN), and Multi-Layer Perceptron (MLP), were used to construct the model and compare the predictive performance of the recommendation system based on hit rate and NDCG. The experimental results showed that suggesting tourist destinations in the Central region was the most effective, with the highest hit rate and NDCG compared to other regions. The average hit rate and NDCG for the five regions were 0.8 and 0.59, respectively. In addition, there has been an analysis of key factors influencing destination selection, such as activity, travel month, travel budget, and the age of tourists, to understand their impact on travel choices in each region of Thailand.
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Machine Learning Based Decision Support System for High-School Study
(2021-01-01)
Chaiwuttisak, Pornpimol
The objectives of this study are to investigate the correlations between personal factors, learning factors family, and economic factors affecting high-school study program selection and also to create and compare models of high-school study program selection with data mining techniques and to develop a decision support system for high-school study program selection with a data mining technique. Data were analyzed by five data mining techniques, and models of high-school study program selection were constructed. These models were then used to construct a decision support system from data mining software called RapidMiner Studio 9. The research findings were as follows personal factors, learning factors family, and economic factors affecting high-school study program selection, and from the result of high-school study program selection, the Decision Tree method, C4.5 algorithm provided the highest accuracy. Therefore, the researcher selected the forecasting model with the Decision Tree method, C4.5 algorithm together with the selection of features with the backward elimination method to create a decision support system.
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Discovery of incremental association rules based on a new FP-growth algorithm
(2019-02-01)
Kreesuradej, Worapoj
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Thurachon, Wannasiri
In this paper, we propose a new FP-Growth algorithm for incremental association rule discovery. We also design a new FPISC-tree based on the FUFP-tree structure. The new FPISC-tree is more suitable for the task of incremental association rule discovery than FUFP-tree structure. The basic ideas of the proposed algorithm are to retrieve the frequent itemsets from the original database and to use their support count in the update of the new support count of the incremental database so that the original paths do not need to be reprocessed as well as to strategically use them to discover frequent itemsets from the FPISC-tree. Experimental results show that the proposed algorithm was able to reduce the number of constructed subtrees and the execution time was significantly less than those of the FP-Growth and FUFP-tree.
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A MULTI-LAYER HYBRID MACHINE LEARNING MODEL for AUTOMATIC SLEEP STAGE CLASSIFICATION
(2018-12-01)
Wongsirichot, Thakerng
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Hanskunatai, Anantaporn
Sleep Stage Classification (SSC) is a standard process in the Polysomnography (PSG) for studying sleep patterns and events. The SSC provides sleep stage information of a patient throughout an entire sleep test. A physician uses results from SSCs to diagnose sleep disorder symptoms. However, the SSC data processing is time-consuming and requires trained sleep technicians to complete the task. Over the years, researchers attempted to find alternative methods, which are known as Automatic Sleep Stage Classification (ASSC), to perform the task faster and more efficiently. Proposed ASSC techniques usually derived from existing statistical methods and machine learning (ML) techniques. The objective of this study is to develop a new hybrid ASSC technique, Multi-Layer Hybrid Machine Learning Model (MLHM), for classifying sleep stages. The MLHM blends two baseline ML techniques, Decision Tree (DT) and Support Vector Machine (SVM). It operates on a newly developed multi-layer architecture. The multi-layer architecture consists of three layers for classifying W, R and N1, N2, N3 in different epoch lengths. Our experiment design compares MLHM and baseline ML techniques and other research works. The dataset used in this study was derived from the ISRUC-Sleep database comprising of 100 subjects. The classification performances were thoroughly reviewed using the hold-out and the 10-fold cross-validation method in both subject-specific and subject-independent classifications. The MLHM achieved a certain satisfactory classification results. It gained 0.694±0.22 of accuracy (AUC=0.822±0.31) in subject-specific classification and 0.942±0.02 of accuracy (AUC=0.920±0.17) in subject-independent classification. The pros and cons of the MLHM with the multi-layer architecture were thoroughly discussed. The effect of class imbalance was rationally discussed towards the classification results.
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Comparative Study of Prediction Models for Final GPA Score: A Case Study of Rajabhat Rajanagarindra University
(2018-09-14)
Putpuek, Narongsak
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Rojanaprasert, Natcha
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Atchariyachanvanich, Kanokwan
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Thamrongthanyawong, Thananya
Recently, the analysis of educational data has become important to all universities. Rajabhat Rajanagarindra University, Thailand, wanted to study and analyze the students' performance based on their personal background. Thus, this research aimed to compare prediction models for the level of the final grade point average (GPA) score of graduated students using the data from the Faculty of Education during the 2010 to 2012 academic years. Two decision tree (C4.5 and ID3) algorithms, plus Naïve Bayes and K-nearest neighbor data mining techniques were adopted to analyze the data according to the CRISP-DM process. Factors that were proposed to influence the graduation GPA include the student's gender, scholarship awarded, previous educational background, admission type, talent and province of high school. The analysis revealed that the Naïve Bayes algorithm gave the best overall accuracy of 43.18%. This could help predict the graduation GPA score of students in the future and support teachers to make educational advice for their students and to develop the student quality in the future.
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An incremental approach to share-frequent itemsets mining
(2018-04-01)
Nawapornanany, Chayanan
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Intakosumy, Sarun
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Boonjingz, Veera
The share-frequent itemsets mining becomes an important topic in the mining of association rules because it can provide useful knowledge such as total quantity of items sold and total profit. In the past, the efficient MCShFI algorithm was successfully proposed to discover complete share-frequent itemsets on a database. When the database is updated, the algorithm can obtain current complete share-frequent itemsets by using the batch approach-mining the whole updated database. To improve mining execution time, we propose a new incremental approach to the problem with the Fast Update (FUP) concept. It obtains the current result by mining only new transactions and updating the previous existing result with this mined result.
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Data mining for positive customer reaction to advertising in social media
(2018-01-01)
Boonjing, Veera
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Pimchangthong, Daranee
Social media has become a source of large amounts of data that is extremely useful when data are analyzed properly. Data mining is one of the known techniques to analyze data to find hidden information from a large amount of available data without having prior hypotheses. The objectives of this research were to (1) find the most important factors influencing positive reactions from customers after seeing online advertising in social media, (2) find the most important factors influencing purchasing merchandise that are advertised online, (3) identify customer clusters characteristics that have positive reaction after seeing online advertising in social media, and (4) identify customer clusters characteristics that purchase merchandise after seeing online advertising in social media. The sample size of 370 is collected by questionnaires using convenience sampling method. Data mining with cluster analysis is used to analyze data. The findings indicate the characteristics of “product conscious” and “price conscious” clusters for customer’s reaction and purchasing after seeing online advertising in social media.
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High candidates generation: A new efficient method for mining share-frequent patterns
(2017-11-01)
Nawapornanan, Chayanan
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Intakosum, Sarun
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Boonjing, Veera
The share frequent patterns mining is more practical than the traditional frequent patternset mining because it can reflect useful knowledge such as total costs and profits of patterns. Mining share-frequent patterns becomes one of the most important research issue in the data mining. However, previous algorithms extract a large number of candidate and spend a lot of time to generate and test a large number of useless candidate in the mining process. This paper proposes a new efficient method for discovering share-frequent patterns. The new method reduces a number of candidates by generating candidates from only high transaction-measure-value patterns. The downward closure property of transaction-measure-value patterns assures correctness of the proposed method. Experimental results on dense and sparse datasets show that the proposed method is very efficient in terms of execution time. Also, it decreases the number of generated useless candidates in the mining process by at least 70%.
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An enhanced incremental association rule discovery with a lower minimum support
(2016-12-01)
Ariya, Araya
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Kreesuradej, Worapoj
In the real world of data, a new set of data has been being inserted into the existing database. Thus, the rule maintenance of association rule discovery in large databases is an important problem. Every time the new data set is appended to an original database, the old rule may probably be valid or invalid. This paper proposed the approach to calculate the lower minimum support for collecting the expected frequent itemsets. The concept idea is applying the normal approximation to the binomial theory. This proposed idea can reduce a process of calculating probability value for all itemsets that are unnecessary. In addition, the confidence interval is also applied to ensure that the collection of expected frequent itemsets is properly kept.
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Incremental multilevel association rule mining of a dynamic database under a change of a minimum support threshold
(2016-01-01)
Pumjun, Nophadon
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Kreesuradej, Worapoj
The proposed algorithm can efficiently deal with multilevel association rules mining of a dynamic database and a current support threshold can be different from the previous task. The experimental results show that the proposed algorithm has better performance than ML-T2 algorithm.

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