Songsak Sriboonchitta’s research while affiliated with Chiang Mai University and other places

This paper proposes a novel framework for clustering mixed numerical and categorical data with missing values. It integrates the imputation and clustering steps into a single process, which results in an algorithm named Clustering Mixed Numerical and Categorical Data with Missing Values (k-CMM). The algorithm consists of three phases. The initialization phase splits the input dataset into two parts based on missing values in objects and attributes types. The imputation phase uses the decision-tree-based method to find the set of correlated data objects. The clustering phase uses the mean and kernel-based methods to form cluster centers at numerical and categorical attributes, respectively. The algorithm also uses the squared Euclidean and information-theoretic-based dissimilarity measure to compute the distances between objects and cluster centers. An extensive experimental evaluation was conducted on real-life datasets to compare the clustering quality of k-CMM with state-of-the-art clustering algorithms. The execution time, memory usage, and scalability of k-CMM for various numbers of clusters or data sizes were also evaluated. Experimental results show that k-CMM can efficiently cluster missing mixed datasets as well as outperform other algorithms when the number of missing values increases in the datasets.

This paper explores the role of weather conditions on tourists’ decision-making. For this purpose, it develops a theoretical framework that integrates how individual decisions are made at the micro level into a dynamic macro-level model with three decision stages: before booking, before departure, and during the trip. The model is empirically tested applying chi-square analysis, logistic regression and multiple correspondence analysis on a sample of inbound tourists visiting China. The results indicate that weather variations affect tourists’ decisions, mainly regarding activities. The specific weather conditions that impact on visitors’ behaviour are different at each decision-making stage. Additionally, the influence of weather becomes smaller as the stages advance. The empirical results also indicate that the effect of weather is affected by tourists’ characteristics. Finally, the results of the paper are used to provide recommendations for including weather consideration into the design and management of tourism products. ____________________ 50 free online copies: https://www.tandfonline.com/eprint/NDXJKURAMNNN7UEQWICU/full?target=10.1080/13683500.2021.1883555

Despite recent efforts, the challenge in clustering categorical and mixed data in the context of big data still remains due to the lack of inherently meaningful measure of similarity between categorical objects and the high computational complexity of existing clustering techniques. While k-means method is well known for its efficiency in clustering large data sets, working only on numerical data prohibits it from being applied for clustering categorical data. In this paper, we aim to develop a novel extension of k-means method for clustering categorical data, making use of an information theoretic-based dissimilarity measure and a kernel-based method for representation of cluster means for categorical objects. Such an approach allows us to formulate the problem of clustering categorical data in the fashion similar to k-means clustering, while a kernel-based definition of centers also provides an interpretation of cluster means being consistent with the statistical interpretation of the cluster means for numerical data. In order to demonstrate the performance of the new clustering method, a series of experiments on real datasets from UCI Machine Learning Repository are conducted and the obtained results are compared with several previously developed algorithms for clustering categorical data.

This paper proposes a novel hybrid forecasting model combining autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) with incorporating moving average and the annual seasonal index for Thailand's cassava export (i.e., native starch, modified starch, and sago). The comprehensive experiments are conducted to investigate the appropriate parameters of the proposed model as well as other forecasting models compared. In particular, the proposed model is experimentally compared to the ARIMA, the ANN and the other hybrid models according to three popular prediction accuracy measures, namely mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The empirical results show that the proposed model gives the lowest error in all three measures for the native starch and the modified starch which are major cassava exported products (98% of the total export volume). However, the Khashei and Bijari's model is the best model for the sago (2% of the total export volume). Therefore, the proposed model can be used as an alternative forecasting method for stakeholders making a decision in cassava international trading to obtain better accuracy in predicting future export of native starch and modified starch which are the majority of the total export.

Let [Formula presented] be a finite field of cardinality [Formula presented], [Formula presented] be an odd positive integer, and denote [Formula presented]. Let [Formula presented]. Then [Formula presented]-constacyclic codes over [Formula presented] are called constacyclic codes over [Formula presented] of Type 2. In this paper, an explicit representation and a complete description for all distinct [Formula presented]-constacyclic codes over [Formula presented] of length [Formula presented] and their dual codes are given. Moreover, explicit formulas for the number of codewords in each code and the number of all such codes are provided respectively. In particular, all distinct self-dual [Formula presented]-constacyclic codes over [Formula presented] of length [Formula presented] are presented precisely. In addition, a complement to a result in Cao et al. (2017) is given.

Let F p m be a finite field of cardinality p m where p is an odd prime, n be a positive integer satisfying gcd(n, p) = 1, and denote R = F p m [u]/u e where e ≥ 4 be an even integer. Let δ, α ∈ F × p m. Then the class of (δ + αu 2)-constacyclic codes over R is a significant subclass of constacyclic codes over R of Type 2. For any integer k ≥ 1, an explicit representation and a complete description for all distinct (δ + αu 2)-constacyclic codes over R of length np k and their dual codes are given. Moreover, formulas for the number of codewords in each code and the number of all such codes are provided respectively. In particular, all distinct (δ + αu 2)-contacyclic codes over F p m [u]/u e of length p k and their dual codes are presented precisely.

Time series forecasting research area mainly focuses on developing effective forecasting models to improve prediction accuracy. An ensemble model composed of autoregressive integrated moving average (ARIMA), artificial neural network (ANN), restricted Boltzmann machines (RBM), and discrete wavelet transform (DWT) is presented in this paper. In the proposed model, DWT first decomposes time series into approximation and detail. Then Khashei and Bijari's model, which is an ensemble model of ARIMA and ANN, is applied to the approximation and detail to extract their both linear and nonlinear components and fit the relationship between the components as a function instead of additive relationship. Furthermore, RBM is used to perform pre-training for generating initial weights and biases based on inputs feature for ANN. Finally, the forecasted approximation and detail are combined to obtain final forecasting. The forecasting capability of the proposed model is tested with three well-known time series: sunspot, Canadian lynx, exchange rate time series. The prediction performance is compared to the other six forecasting models. The results indicate that the proposed model gives the best performance in all three data sets and all three measures (i.e. MSE, MAE and MAPE).

Researchers have proposed statistical regression models that analyse on-line review data to identify attractive attributes of a product or service. This research has the same aim, but with an approach based on machine learning models instead of statistical models. The proposed approach first extracts attribute-level sentiments from the review text by natural language processing techniques, then derives features that reflect the non-linear relations between attribute performance and customer satisfaction based on the sentiments. The non-linear features are fed to the Support Vector Machine (SVM) model to train predictive attractive attribute classifiers. The proposed approach is evaluated on a hotel review dataset crawled from TripAdvisor. The experiment results indicate that the classifiers reach a precision of 79.3% and outperform the existing statistical models by a margin of over 10%.

In this study, we propose a non-linear model for explaining the relationship between the dependent and the independent variables beyond the conditional mean. We extend the kink approach to expectile regression thus the model provides a more flexible means to explain the non-linear relationship in the model across different expectile indices. We also introduce the sup-F statistic test for the existence of kink effect in each expectile. The simulation and application studies are also proposed to examine the performance of our model. We apply our methodology to study the input factor affecting service sector growth in Asian economy. The use of this model allows us to identify and explore the non-linear labour effect on the service output. We can find both labour effect and kink effect present over a range of expectiles in the service output in this application.

For any odd prime p, negacyclic codes of length 4ps over the finite commutative chain ring Fpm+uFpm are investigated. The algebraic structures of such codes are classified and completely determined. As an application, the number of codewords and the dual of each negacyclic code are obtained. Simpler structure of cyclic codes of length 4ps over Fpm+uFpm is also noted. Among others, some self-dual negacyclic and cyclic codes of length 4ps over Fpm+uFpm are provided.