Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the period of 2000-2006 covering 24 journals and proposes a classification scheme to classify the articles. Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM. Eighty-seven articles were subsequently selected, reviewed and classified. Each of the 87 selected papers was categorized on four CRM dimensions (Customer Identification, Customer Attraction, Customer Retention and Customer Development) and seven data mining functions (Association, Classification, Clustering, Forecasting, Regression, Sequence Discovery and Visualization). Papers were further classified into nine sub-categories of CRM elements under different data mining techniques based on the major focus of each paper. The review and classification process was independently verified. Findings of this paper indicate that the research area of customer retention received most research attention. Of these, most are related to one-to-one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM. Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.

http://cjou.im.tku.edu.tw/bi2009/DM-usage.pdf

Review: Application of data mining techniques in customer relationship management: A literature review and classification

We consider heuristic algorithms for the resource-constrained project scheduling problem. Starting with a literature survey, we summarize the basic components of heuristic approaches. We briefly describe so-called X -pass methods which are based on priority rules as well as metaheuristic algorithms. Subsequently, we present the results of our in-depth computational study. Here, we evaluate the performance of several state-of-the-art heuristics from the literature on the basis of a standard set of test instances and point out to the most promising procedures. Moreover, we analyze the behavior of the heuristics with respect to their components such as priority rules and metaheuristic strategy. Finally, we examine the impact of problem characteristics such as project size and resource scarceness on the performance.

Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem

Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Knowledge Discovery and Data Mining is a very dynamic research and development area that is reaching maturity. As such, it requires stable and well-defined foundations, which are well understood and popularized throughout the community. This survey presents a historical overview, description and future directions concerning a standard for a Knowledge Discovery and Data Mining process model. It presents a motivation for use and a comprehensive comparison of several leading process models, and discusses their applications to both academic and industrial problems. The main goal of this review is the consolidation of the research in this area. The survey also proposes to enhance existing models by embedding other current standards to enable automation and interoperability of the entire process.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0269888906000737

A survey of Knowledge Discovery and Data Mining process models

Stock prices as time series are non-stationary and highly-noisy due to the fact that stock markets are affected by a variety of factors. Predicting stock price or index with the noisy data directly is usually subject to large errors. In this paper, we propose a new approach to forecasting the stock prices via the Wavelet De-noising-based Back Propagation (WDBP) neural network. An effective algorithm for predicting the stock prices is developed. The monthly closing price data with the Shanghai Composite Index from January 1993 to December 2009 are used to illustrate the application of the WDBP neural network based algorithm in predicting the stock index. To show the advantage of this new approach for stock index forecast, the WDBP neural network is compared with the single Back Propagation (BP) neural network using the real data set.

Forecasting stock indices with back propagation neural network

In the past two decades, the heavy environmental loading has led to the deterioration of air quality in Taiwan. The task of controlling and improving air quality has attracted a great deal of national attention. The Taiwanese government has since set up the National Air Quality Monitoring Network (TAQMN) to monitor nationwide air quality and adopted an array of measures to combat this problem. This study applies data mining to uncover the hidden knowledge of air pollution distribution in the voluminous data retrieved from monitoring stations in TAQMN. The mining process consists of data acquisition from Web sites of 71 data gathering stations nationwide, data pre-processing using multi-scale wavelet transforms, data pattern identification using cluster analysis, and final analysis in mapping the identified clusters to geographical locations. The application of multi-scale wavelet transforms contributes greatly in removing noises and identifying the trend of data. In addition, the proposed two-level self-organization map neural network demonstrates its ability in identifying clusters on the high-dimensional wavelet-transformed space. The identified distribution of suspended particulate PM10 represents a complete, national picture of the present air quality situation, which contrasts the present pollution districts, and could serve as an important reference for government agencies in evaluating present and devising future air pollution policies.

https://isiarticles.com/bundles/Article/pre/pdf/22052.pdf

Data mining to aid policy making in air pollution management

The recent deregulation of telecommunication industry by the Taiwanese government has brought about the acute competition for Internet Service Providers (ISP). Taiwan's ISP industry is characterized by the heavy pressure for raising revenue after hefty capital investments of last decade and the lack of knowledge to develop competitive strategies. To attract subscribers, all ISP dealers are making an all-out effort to improve their service management. This study proposes a Business Intelligence process for ISP dealers in Taiwan to assist management in developing effective service management strategies. We explore the customers' usage characteristics and preference knowledge through applying the attribute-oriented induction (AOI) method on IP traffic data of users. Using the self-organizing map (SOM) method, we are able to divide customers into clusters with different usage behavior patterns. We then apply RFM modeling to calibrate customers' value of each cluster, which will enable the management to develop direct and effective marketing strategies. For network resource management, this research mines the facility utilization over various administrative districts of the region, which could assist management in planning for effective network facilities investment. With actual data from one major ISP, we develop a BI decision support system with visual presentation, which is well received by its management staff.

http://im1.im.tku.edu.tw/~cjou/bi2009/3.pdf

Business intelligence approach to supporting strategy-making of ISP service management

This study presents a new approach in developing an expert system to assess financial quality of an enterprise. In dealing with the complexity of financial statement analysis, this approach separates knowledge content into domain knowledge of financial statements and operational knowledge of analysts' analytical process. The former represents the well defined relationships of accounting items of financial statements. The latter represents the reasoning processes that use the domain knowledge to appraise the quality of given financial statements. We apply ontology to model the content of domain knowledge, and use decision rules to represent operational knowledge. This system integrates Protege, as domain knowledge base, and JESS, as operational knowledge base, into one complete expert system. The performance validity of the system was verified with financial statements from Taiwanese stock market.

The development of an ontology-based expert system for corporate financial rating

This study investigates optimal policies for determining price and warranty length when free replacement of defective items is the business policy and the demand is dependent on price, warranty, and cumulative sales. We apply optimal control theory to a profit-maximization model that takes into consideration the expected warranty cost per item. The maximization of market values is based on a pre-determined life cycle and spans across both expansion and saturation phases of the market. We further investigate the optimal policies for a number of basic lifetime distributions, when the demand function is under the setting of separable and logistic growth. We found that as the growth of a product enters different phases of the market, optimal decision policies are characterized by simultaneously increasing or reducing both price and warranty length.

Application of optimal control theory to product pricing and warranty with free replacement under the influence of basic lifetime distributions

This paper introduces an admissible heuristic search algorithm — Search and Learning Algorithm (SLA*). SLA* is developed from the work presented by Korf in the Learning-Real-Time-Algorithm (LRTA*). We retain the major elements of Korfs work in LRTA*, and improve its performance by incorporating a review component to fully reflect the effect the learning of new heuristic from front states has upon the previous states. The combined strategy of search, learning, and review has enabled this algorithm to accumulate knowledge continuously through guided expansion, and to identify better search directions in any stage of nodes expansion. With the assumption of non-overestimating initial estimates for all nodes to the goal, this algorithm is able to find an optimal solution in a single problem solving trial with good efficiency. We provide a proof for the optimality of the solution.

Li-Yen Shue

Papers

Data mining to aid policy making in air pollution management

Business intelligence approach to supporting strategy-making of ISP service management

The development of an ontology-based expert system for corporate financial rating

Application of optimal control theory to product pricing and warranty with free replacement under the influence of basic lifetime distributions

An Admissible Heuristic Search Algorithm