Chuang-Cheng Chiu

A purchase-based market segmentation methodology

Abstract Market segmentation is critical for a good marketing and customer relationship management program. Traditionally, a marketer segments a market using general variables such as customer demographics and lifestyle. However, several problems have been identified and make the segmentation result unreliable. This paper develops a novel market segmentation methodology based on product specific variables such as purchased items and the associative monetary expenses from the transactional history of customers to resolve these problems. A purchase-based similarity measure, clustering algorithm, and clustering quality function are defined in this paper. A genetic algorithm approach is adopted to ensure that customers in the same cluster have the closest purchase patterns. After completing segmentation, a designated RFM model is used to analyze the relative profitability of each customer cluster. The findings from a practical marketing implementation study will also be discussed.

Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm

K-means is one of the most popular and widespread partitioning clustering algorithms due to its superior scalability and efficiency. Typically, the K-means algorithm treats all features fairly and sets weights of all features equally when evaluating dissimilarity. However, a meaningful clustering phenomenon often occurs in a subspace defined by a specific subset of all features. To address this issue, this paper proposes a novel feature weight self-adjustment (FWSA) mechanism embedded into K-means in order to improve the clustering quality of K-means. In the FWSA mechanism, finding feature weights is modeled as an optimization problem to simultaneously minimize the separations within clusters and maximize the separations between clusters. With this objective, the adjustment margin of a feature weight can be derived based on the importance of the feature to the clustering quality. At each iteration in K-means, all feature weights are adaptively updated by adding their respective adjustment margins. A number of synthetic and real data are experimented on to show the benefits of the proposed FWAS mechanism. In addition, when compared to a recent similar feature weighting work, the proposed mechanism illustrates several advantages in both the theoretical and experimental results.

A Web services-based collaborative scheme for credit card fraud detection

A Web services-based collaborative scheme for credit card fraud detection is proposed. With the proposed scheme, participant banks can share the knowledge about fraud patterns in a heterogeneous and distributed environment and further enhance their fraud detection capability and reduce financial loss.

A case-based reasoning system for PCB defect prediction

The manufacturing process for a new Printed Circuit Board (PCB) design is often instable and might generate a number of defects during the complicated production process. Defects reduce the yield rate and increase the production costs. Although skilled engineers can predict the possible defect items for a new PCB product, this approach requires strong engineering experience and is time consuming. To conquer this problem, this research applies case-based reasoning (CBR) methodology to develop a defect prediction system for new PCB products. In the CBR system, each case is represented using the design specifications, defect items and corresponding costs. A vantage-based case indexing mechanism is developed to accelerate the case retrieval efficiency. In addition, a reasoning algorithm that considers the defect cost is proposed to infer the defect items that are interesting to PCB manufacturers. The system performance is analyzed to show the efficiency and accuracy of the proposed system. A practical implementation using a case-base provided by a PCB manufacturer is demonstrated.

A k-Anonymity Clustering Method for Effective Data Privacy Preservation

Data privacy preservation has drawn considerable interests in data mining research recently. The k-anonymity model is a simple and practical approach for data privacy preservation. This paper proposes a novel clustering method for conducting the k-anonymity model effectively. In the proposed clustering method, feature weights are automatically adjusted so that the information distortion can be reduced. A set of experiments show that the proposed method keeps the benefit of scalability and computational efficiency when comparing to other popular clustering algorithms.

A case-based reasoning system for PCB principal process parameter identification

The Printed Circuit Board (PCB) manufacturing process usually consists of lengthy production activities. Each activity is controlled by a number of process parameters. Although numerous process parameters must be determined before fabrication, only a number of parameters called principal process parameters because they affect the quality of a PCB product. As long as the principal process parameters are identified efficiently and controlled well, the manufacturing lead-time can be shortened and the quality of the new PCB product can be assured. This research proposes a Case-Based Reasoning (CBR) system to infer the principal process parameters for a new PCB product. Each case in the case-base stores design specifications, process parameters, and the corresponding production quality specifications. A Significant Nearest Neighbor (SNN) search is developed to retrieve similar cases from a case-base. A Mutual Correlation Parameter Selection (MCPS) method and a correlation-based parameter setting method are developed to identify the principal parameters and infer their reasonable value range. A set of experiments and a practical implementation case are demonstrated to show the efficiency and accuracy of the proposed system.

A weighted feature C-means clustering algorithm for case indexing and retrieval in cased-based reasoning

A successful Case-Based Reasoning (CBR) system highly depends on how to design an accurate and efficient case retrieval mechanism. In this research we propose a Weighted Feature C-means clustering algorithm (WF-C-means). to group all prior cases in the case base into several clusters. In WF-C-means, the weight of each feature is automatically adjusted based on the importance of the feature to clustering quality. After executing WF-C-means, the dissimilarity definition adopted by K-Nearest Neighbor (KNN) search method to retrieve similar prior cases for a new case becomes refined and objective because the weights of all features adjusted by WF-C-means can be involved in the dissimilarity definition. On the other hand, based on the clustering result of WF-C-means, this research proposes a cluster-based case indexing scheme and its corresponding case retrieval strategy to help KNN retrieving the similar prior cases efficiently. Through our experiments, the efforts of this research are useful for real world CBR systems.

An efficient conserved region detection method for multiple protein sequences using principal component analysis and wavelet transform

This paper proposes an efficient conserved region detection method for multiple protein sequences. Instead of detecting conserved regions directly from the set of all participatory protein sequences, the proposed method separates the detection process as two stages. In the fist stage, a serial of principal component analysis (PCA) techniques are applied to infer the common ancestor protein from the participatory proteins based on a hypothetical evolutionary history. Then, wavelet transform is employed to derive conserved regions from the common ancestor protein in the second stage. The detected conserved regions are considered as the common conserved regions of the original protein sequences. A set of experiments indicate that the two stage strategy makes the proposed method not only prevents the residue divergence problem but also increases the detection accuracy and efficiency.

A clustering-oriented star coordinate translation method for reliable clustering parameterization

When conducting a clustering process, users are generally concerned whether the clustering result is reliable enough to reflect the actual clustering phenomenon. The number of clusters and initial cluster centers are two critical parameters that influence the reliability of clustering results highly. We propose a Clustering-Oriented Star Coordinate Translation (COSCT) method to help users determining the two parameters more confidently. Through COSCT all objects from a multi-dimensional space are adaptively translated to a 2D starcoordinate plane, so that the clustering parameterization can be easily conducted by observing the clustering phenomenon in the plane. To enhance the cluster-displaying quality of the star-coordinate plane, the feature weighting and coordinate arrangement procedures are developed. The effectiveness of the COSCT method is demonstrated using a set of experiments.

A Real-time Mobile System for Fetal Heart Rate Monitoring and Fetal Distress Detection

This paper proposes a mobile system for real-time fetal heart rate (FHR) monitoring and fetal distress detection. In the proposed system, FHR values are collected using machine vision techniques and analyzed using a pattern matching approach. When a fetal distress is detected, a medical alarm will automatically notify medical experts through a mobile GSM network. Through the proposed system, not only a pregnant woman can track the healthy status of her baby, but also medical experts can allocate medical resources in time. The implementation result shows that the developed mobile system can be a helpful application for medical management.