Shih-Yen Lin

Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach

The effective recognition of unnatural control chart patterns (CCPs) is a critical issue in statistical process control, as unnatural CCPs can be associated with specific assignable causes adversely affecting the process. Machine learning techniques, such as artificial neural networks (ANNs), have been widely used in the research field of CCP recognition. However, ANN approaches can easily overfit the training data, producing models that can suffer from the difficulty of generalization. This causes a pattern misclassification problem when the training examples contain a high level of background noise (common cause variation). Support vector machines (SVMs) embody the structural risk minimization, which has been shown to be superior to the traditional empirical risk minimization principle employed by ANNs. This research presents a SVM-based CCP recognition model for the on-line real-time recognition of seven typical types of unnatural CCP, assuming that the process observations are AR(1) correlated over time. Empirical comparisons indicate that the proposed SVM-based model achieves better performance in both recognition accuracy and recognition speed than the model based on a learning vector quantization network. Furthermore, the proposed model is more robust toward background noise in the process data than the model based on a back propagation network. These results show the great potential of SVM methods for on-line CCP recognition.

A case study of applying LRFM model in market segmentation of a children's dental clinic

Dental services marketing has become more and more crucial in Taiwan after Taiwans entrance of the World Trade Organization and the implementation of National Health Insurance (NHI) program. This paper develops an extended RFM (recency, frequency, and monetary) model, namely LRFM (length, recency, frequency, and monetary) model, by adopting self-organizing maps (SOM) technique for a childrens dental clinic in Taiwan to segment its dental patients. Twelve clusters are recommended for the overall 2258 patients. The average values of LRF are computed for each cluster and the overall patients, excluding monetary covered by NHI program. The values of LRF variables for each cluster greater than those of the overall average are identified. The results show that three clusters having the above average LRF values (454 patients) can be viewed as core patients.

Applying Data Mining and RFM Model to Analyze Customers' Values of a Veterinary Hospital

Due to fierce competition, veterinary hospitals have to maintain good relationship with their existing customers and attract new customers. In order to identify critical customers, data mining techniques particularly cluster analysis are viewed as a vital tool to facilitate customer relationship management. This study uses a veterinary hospital located in Taichung City, Taiwan as an example by analyzing its transactions data focusing solely on dogs in 2014 with 4,472 customers. Recency, frequency, and monetary are the three input variables for cluster analysis. A combination of self-organizing maps and K-means method is used for cluster analysis. The results show that seven out of twelve clusters are found to be the best or loyal customers, while three clusters are uncertain or lost customers. Two clusters with relatively higher recency values than average can be viewed as new customers. When customers are classified, this veterinary hospital can provide different marketing strategies to meet different customer needs.

Analyzing Consumption Behaviors of Pet Owners in a Veterinary Hospital

The purpose of this study is to identify different consumption behaviors of pet owners in a veterinary hospital so as to provide proper marketing strategies. A case study was conducted by combining data mining techniques and RFM model for a veterinary hospital located in Taichung City, Taiwan by examining its transactions data focusing on pet mice in 2014. The development of marketing strategies for the veterinary hospital is important to improve its service quality and strengthen the positive relationship between the pet owners and the case veterinary hospital.