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Enhancing Psychological Well-Being Assessment Through Data Mining: A Case Study from Thailand
(2025-04-01)
Treearpornwong, Asamaporn
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Kantathanawat, Thiyaporn
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Charoentham, Mai
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Pimdee, Paitoon
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Sukkamart, Aukkapong
This study examines the psychological well-being (PWB) of lower secondary school students in Bangkok’s Secondary Educational Service Area Offices (SESAO) 1 and 2, using data mining techniques to analyze key influencing factors and develop a culturally adapted PWB questionnaire. The research framework is based on six components: autonomy, environmental mastery, personal growth, positive relationships, life purpose, and self-acceptance. Data were collected from 2543 students in the 2023 academic year and analyzed using the Waikato Environment for Knowledge Analysis (WEKA) program and the JRip rule-based classification model. Results indicate that personal growth is the most predictive in the classification performance of PWB, followed by positive relationships and life purpose. A newly developed PWB questionnaire was tested for reliability, with the Supplied Test Set (80:20) method yielding strong performance metrics, including accuracy (90.18%), precision (69.00%), recall (90.90%), and F-measure (78.40%). This study demonstrates data mining’s effectiveness in identifying factors influencing adolescent PWB within the Thai context. The findings provide educators and policymakers with insights for fostering student well-being and contribute to research by offering a validated, culturally relevant assessment tool.
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Differential Evolution for Classification: A Novel Classifier Technique in Data Mining
(2024-01-01)
Farda, Irfan
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Thammano, Arit
In the realm of Data Mining, the pursuit of innovative classification methodologies remains crucial for advancing robust techniques in handling complex and diverse datasets. This paper explores the application of Differential Evolution (DE), a powerful optimization algorithm, as a unique and effective optimization-based standalone classifier. Our exploration focused on harnessing DE's intrinsic capabilities, adapting it into a classifier while preserving its distinctiveness. The fundamental principle of DE for classification involves iteratively optimizing the center point for each class using DE operators, which are specialized mechanisms for exploring and refining solutions, and utilizing these optimized points for making predictions. The effectiveness of our model was evaluated on ten classification datasets from the UCI Machine Learning Repository and compared against three other classification methods: KNN, ZMP, and BPNN. Experimental results underscore the competitive performance of our proposed model, emphasizing the potential of DE in effectively addressing classification challenges.
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Incremental Association Rule Mining with a Fast Incremental Updating Frequent Pattern Growth Algorithm
(2021-01-01)
Thurachon, Wannasiri
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Kreesuradej, Worapoj
One of the most challenging tasks in association rule mining is that when a new incremental database is added to an original database, some existing frequent itemsets may become infrequent itemsets and vice versa. As a result, some previous association rules may become invalid and some new association rules may emerge. We designed a new, more efficient approach for incremental associationrule mining using a Fast Incremental Updating Frequent Pattern growth algorithm (FIUFP-Growth), a new Incremental Conditional Pattern tree (ICP-tree), and a compact sub-tree suitable for incrementalmining of frequent itemsets. This algorithm retrieves previous frequent itemsets that have already been mined from the original database and their support counts then use them to efficiently mine frequent itemsets from the updated database and ICP-tree, reducing the number of rescans of the original database. Our algorithm reduced usages of resource and time for unnecessary sub-tree construction compared to individual FP- Growth, FUFP-tree maintenance, Pre-FUFP, and FCFPIM algorithms. From the results, at 3% minimum support threshold, the average execution time for pattern growth mining of our algorithm performs 46% faster than FP- Growth, FUFP-tree, Pre-FUFP, and FCFPIM. This approach to incremental association rule mining and our experimental findings may directly benefit designers and developers of computer business intelligence methods.
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Investigation of Factors Affecting the Performance of in silico Volume Distribution QSAR Models for Human, Rat, Mouse, Dog & Monkey
(2019-10-01)
Simeon, Saw
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Montanari, Dino
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Gleeson, Matthew Paul
Volume of distribution (Vd<inf>ss</inf>) is a measure of how effectively a drug molecule is distributed throughout the body. Along with the clearance, it determines the half-life and therefore the drug dosing interval. A number of different pre-clinical approaches are available to predict the Vd<inf>ss</inf> in human including quantitative structure activity relationship (QSAR) models. Vd<inf>ss</inf> QSAR models have been reported for human and rat, but not important pre-clinical species including dog, mouse and monkey. In this study, we have generated Vd<inf>ss</inf> QSAR model on the human and commonly used pre-clinical species, each of which differs in terms of size, chemical diversity and data quality. We discuss the model performance by species, assess the effect the domain of applicability and the relative merits of building chemical series-specific models. In addition, we compare the intrinsic variability of the experimental logVd<inf>ss</inf> data (∼1.2 fold error) to in-vivo interspecies differences (∼2 fold error) and in silico based models (∼3 fold error). This prompted us to explore whether one species could be used to predict another, particularly where little data for that species is available. i. e. does the expansion in domain of applicability prove beneficial over and above any deterioration due to the use of response values from an alternative species.
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Noise and Vibration Analysis of Dry-Type Power Transformer for Monitoring and Data Mining Applications
(2019-08-01)
Boonseng, C.
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Boonseng, R.
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Kularbphettong, K.
An important source of transformer noise is magnetostrictive vibration of the magnetic core. This paper outlines some important effects of vibration on aging due to the deterioration of insulation by on-line measurement. The acceleration in the XYZ axis is telling the model of the vibrational force. A large amount of data can be collected as a database for maintenance decisions and take care of electrical insulation. Finally, collect data into the data management process using data mining. Storage data is much smaller approximately 1.17% as compared to all data.
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A Technique for Estimating Updated Frequent Itemsets in ESC-Growth Algorithm
(2019-07-01)
Kreesuradej, Worapoj
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Thurachon, Wannasiri
In discovering association rules from a dynamic database, iteration through the frequent itemsets requires significant resources and computational time for construction of sub-trees, sub-tree traversal and generation of conditional pattern bases, and it is quite possible that no updated frequent itemsets may have been found at all, resulting in a waste of resources and computational time. We describe a technique for estimating the support count for the itemsets for the next iteration of discovery of the frequent itemsets by our ESC-Growth Algorithm. This technique reduces the need to construct a new sub-tree and next discovery step. If no frequent itemsets in the updated database have been found in the next iteration, ESC-Growth will not construct a new sub-tree and will stop discovering new frequent itemsets in that iteration, reducing the waste of resources and computational time. We measured execution time and sub-tree counts for FP-Growth, FUFP-tree, FPISC-Growth and ESCGrowth on the same synthetic dataset; we found that, at 5% minimum support threshold, ESC-Growth used only 40.3, 96.5, and 99.6% of the execution time required by FP-Growth, FUFPtree and FPISC-Growth, respectively.
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Financial Latent Dirichlet Allocation (FinLDA): Feature Extraction in Text and Data Mining for Financial Time Series Prediction
(2019-01-01)
Kanungsukkasem, Nont
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Leelanupab, Teerapong
News has been an important source for many financial time series predictions based on fundamental analysis. However, digesting a massive amount of news and data published on the Internet to predict a market can be burdensome. This paper introduces a topic model based on latent Dirichlet allocation (LDA) to discover features from a combination of text, especially news articles and financial time series, denoted as Financial LDA (FinLDA). The features from FinLDA are served as additional input features for any machine learning algorithm to improve the prediction of the financial time series. We provide posterior distributions used in Gibbs sampling for two variants of the FinLDA and propose a framework for applying the FinLDA in a text and data mining for financial time series prediction. The experimental results show that the features from the FinLDA empirically add value to the prediction and give better results than the comparative features including topic distributions from the common LDA.
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Reservoir inflow forecasting using ID3 and C4.5 decision tree model
(2017-10-26)
Charoenporn, Pattama
Decision tree model is one of data mining method for builds classification models in the form of a tree structure. These methods are produced various ways of splitting a data set into branch like segments that call nodes. Today, forecasting method is very importance for every side especially agriculture. Because some farmers who want to predict their crops for each semester. This paper describes about method for forecast daily inflow to reservoir in order to present new method for predication. We prepare 1000 data sets for analysis with reservoir prototype. And then used training set testing ID3 and C4.5 algorithms for choose the best algorithm to create reservoir prototype. The results show that ID3 algorithm is the best way for forecasting data then we will create the reservoir prototype in order to forecasting water pass reservoir, and can indicate level of water in very severe, severe, or less. After that we bring reservoir prototype to test by users, and all of user test reservoir prototype and suggest that 67% satisfy in learning ability, 80% moderately satisfied in control.
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Frequent itemsets mining using random walks for record insertion and deletion
(2017-02-23)
Thusaranon, Panita
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Kreesuradej, Worapoj
In Association rules mining, the task of finding frequent itemsets in dynamic database is very important because the updates may not only invalidate some existing rules but also make other rules relevant. In this paper, we propose a new algorithm to maintain frequent itemsets of a dynamic database in the case of record insertion as well as deletion simultaneously. Basically, the proposed algorithm maintains not only the support counts of frequent itemsets but also the support counts of prospective frequent itemsets, i.e., infrequent itemsets that promise to be frequent in the future, in an original database. Prospective frequent itemsets, which are obtained by using the principle of Random Walks, can help to reduce a number of times to rescan the original database.
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Maintenance of multi-level association rules discovery in dynamic database under a change of support threshold
(2016-01-13)
Pumjun, Nophadon
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Kreesuradej, Worapoj
An association rule mining is often performed with a dynamic database and hierarchical items. The big problem of data mining process is a maintenance association rules while the database always changing. The purpose of this study is to extend the MLUp algorithm which can maintain a multilevel association rules discovery at the same minimum support threshold. In general, several mining tasks are required to deal with different support thresholds. MLUpCS can deal with a maintaining of mining multilevel association rules in dynamic databases under the different support threshold without re-mine a whole database. The result of MLUpCS algorithm experiment has shown how better performance than ML-T2 algorithm. The experimental results show the superior performance of MLUpCS when compared with ML-T2.

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