R.J. Kuo

Integration of self-organizing feature map and K-means algorithm for market segmentation

Cluster analysis is a common tool for market segmentation. Conventional research usually employs the multivariate analysis procedures. In recent years, due to their high performance in engineering, artificial neural networks have also been applied in the area of management. Thus, this study aims to compare three clustering methods: (1) the conventional two-stage method, (2) the self-organizing feature maps and (3) our proposed two-stage method, via both simulated and real-world data. The proposed two-stage method is a combination of the self-organizing feature maps and the K-means method. The simulation results indicate that the proposed scheme is slightly better than the conventional two-stage method with respect to the rate of misclassification, and the real-world data on the basis of Wilks Lambda and discriminant analysis.

Application of particle swarm optimization to association rule mining

In the area of association rule mining, most previous research had focused on improving computational efficiency. However, determination of the threshold values of support and confidence, which seriously affect the quality of association rule mining, is still under investigation. Thus, this study intends to propose a novel algorithm for association rule mining in order to improve computational efficiency as well as to automatically determine suitable threshold values. The particle swarm optimization algorithm first searches for the optimum fitness value of each particle and then finds corresponding support and confidence as minimal threshold values after the data are transformed into binary values. The proposed method is verified by applying the FoodMart2000 database of Microsoft SQL Server 2000 and compared with a genetic algorithm. The results indicate that the particle swarm optimization algorithm really can suggest suitable threshold values and obtain quality rules. In addition, a real-world stock market database is employed to mine association rules to measure investment behavior and stock category purchasing. The computational results are also very promising.

A decision support system for sales forecasting through fuzzy neural networks with asymmetric fuzzy weights

Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g., promotion, cause a sudden change in the sales pattern. Thus, this study utilizes fuzzy logic a proposed fuzzy neural network (FNN) for the sake of learning fuzzy IF–THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the forecast from ANN using the time series data and the promotion length through the other ANN. Model evaluation results indicate that the proposed system can more accurately perform than the conventional statistical method and single ANN.

An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination

Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.

Application of particle swarm optimization algorithm for solving bi-level linear programming problem

Bi-level linear programming is a technique for modeling decentralized decision. It consists of the upper-level and lower-level objectives. This paper attempts to develop an efficient method based on particle swarm optimization (PSO) algorithm with swarm intelligence. The performance of the proposed method is ascertained by comparing the results with genetic algorithm (GA) using four problems in the literature and an example of supply chain model. The results illustrate that the PSO algorithm outperforms GA in accuracy.

Integration of self-organizing feature maps neural network and genetic K-means algorithm for market segmentation

This study is dedicated to proposing a novel two-stage method, which first uses Self-Organizing Feature Maps (SOM) neural network to determine the number of clusters and the starting point, and then uses genetic K-means algorithm to find the final solution. The results of simulated data via a Monte Carlo study show that the proposed method outperforms two other methods, K-means and SOM followed by K-means (Kuo, Ho & Hu, 2002a), based on both within-cluster variations (SSW) and the number of misclassification. In order to further demonstrate the proposed approachs capability, a real-world problem of the fright transport industry market segmentation is employed. A questionnaire is designed and surveyed, after which factor analysis extracts the factors from the questionnaire items as the basis of market segmentation. Then the proposed method is used to cluster the customers. The results also indicate that the proposed method is better than the other two methods

A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm

Abstract This research attempts to develop a decision support system for order selection. The proposed system is able to integrate both the quantitative and qualitative factors together. For the qualitative factors, the fuzzy IF–THEN rules are summarized from the questionnaire survey for the production experts and learned by a proposed fuzzy neural network (FNN) with initial weights generated by real-coded genetic algorithm (GA). Then, a feedforward artificial neural network (ANN) with error back-propagation (EBP) learning algorithm is employed to integrate the above two parts together. Both the simulation and real-life problem provided by an internationally OEM company results show that the proposed FNN can well learn the fuzzy IF–THEN rules. In addition, real-coded GA is proved to be better than the binary GA both in speed and accuracy. Considering both the quantitative and qualitative factors has more accurate results compared with considering only the quantitative factors.

Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering

This study proposes an evolutionary-based clustering algorithm based on a hybrid of genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) for order clustering in order to reduce surface mount technology (SMT) setup time. Simulational results via Iris, Glass, Vowel and Wine benchmark data sets indicate that the proposed evolutionary-based clustering algorithm is more accurate than the GA-based and PSOA-based clustering algorithms. In addition, the model evaluation results which use order information provided by an international industrial personal computer (PC) manufacturer show that the proposed algorithm is also superior to GA-based and PSOA-based clustering algorithms. Through order clustering, scheduling orders that belong to the same cluster together can reduce production time as well as machine idle time.

Integration of ART2 neural network and genetic K-means algorithm for analyzing web browsing paths in electronic commerce

Neural networks and genetic algorithms are useful for clustering analysis in data mining. Artificial neural networks (ANNs) and genetic algorithms (GAs) have been applied in many areas with very promising results. Thus, this study uses adaptive resonance theory 2 (ART2) neural network to determine an initial solution, and then applies genetic K-means algorithm (GKA) to find the final solution for analyzing Web browsing paths in electronic commerce (EC). The proposed method is compared with ART2 followed by K-means.In order to verify the proposed method, data from a Monte Carlo Simulation are used. The simulation results show that the ART2 + GKA is significantly better than the ART2 + K-means, both for mean within cluster variations and misclassification rate. A real-world problem, a recommendation agent system for a Web PDA company, is investigated. In this system, the browsing paths are used for clustering in order to analyze the browsing preferences of customers. These results also show that, based on the mean within-cluster variations, ART2 + GKA is much more effective.

Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network

On-line tool wear estimation plays a very critical role in industry automation for higher productivity and product quality. In addition, appropriate and timely decision for tool change is significantly required in the machining systems. Thus, this paper is dedicated to develop an estimation system through integration of two promising technologies, artificial neural networks (ANN) and fuzzy logic. An on-line estimation system consisting of five components: (1) data collection; (2) feature extraction; (3) pattern recognition; (4) multi-sensor integration; and (5) tool/work distance compensation for tool flank wear, is proposed herein. For each sensor, a radial basis function (RBF) network is employed to recognize the extracted features. Thereafter, the decisions from multiple sensors are integrated through a proposed fuzzy neural network (FNN) model. Such a model is self-organizing and self-adjusting, and is able to learn from the experience. Physical experiments for the metal cutting process are implemented to evaluate the proposed system. The results show that the proposed system can significantly increase the accuracy of the product profile.