【正文】 tion including knowledge rules, constraints and regularities. Data mining, a key step in Knowledge Discovery in Databases (KDD), involves the application of speci?c algorithms for pattern extraction. Various successful applications have been reported in areas such as the web, marketing, ?nance and banking. Currently, businesses face the challenge of a constantly evolving market where customer needs are changing all the time. Hence, instead of targeting all customers equally, enterprises can select only those customers who meet certain pro?tability criteria based on their individual needs or purchasing behaviors. As a result, the discovered information can be ascertained to support better decisionmaking in marketing. Consequently, one can de?ne data mining in customer pro?ling simply as being the technology that allows building customer pro?les each describing the speci?c habits, attitudes and behavior of a group of customers. Some of the di?culties faced by data mining techniques for customer pro?ling are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for the automatic generation of customer models that simulate human decisionmaking. Several Arti?cial Intelligence techniques have been proposed in the literature to address this problem. In fact, models using Bayesian works, decision trees, support vector machines, arti?cial neural works, and association rules have been used in many industrial applications in order to develop customer pro?les. Hereafter, we will outline some of the research activities for customer pro?ling to give the novice reader some background in the ?eld. For an exhaustive review of existing approaches, we refer the interested reader to the specialized literature. 6 The model in Ref. 17 proposes an integrated data mining and behavioral scoring model to manage existing credit card customers in a bank. A selfanizing map was used to identify groups of customers based on repayment behavior and recency, frequency, moary behavioral and scoring predictors. It also classi?ed bank customers into three major pro?table groups of customers. The resulting groups of customers were then pro?led by customer’s feature attributes determined using an apriori association rule inducer. Other works are also developed in retail marketing because understanding changes in customer behavior in the dynamic retail market can help managers to establish e?ective promotion campaigns. The model in Ref. 5 integrates customer behavioral variables, demographic variables, and transaction database to establish a method of mining changes in customer behavior. For mining change patterns, two extended measures of similarity and unexpectedness are designed to analyze the degree of resemblance between patterns at di?erent time periods. Customer behavior patterns are ?rst identi?ed using association rule mining. Once the association rules are discovered, the changes in customer behavior are identi?ed by paring two sets of association rules generated from two data sets at di?erent periods. Based on previous studies, changes in customer behavior include emerging patterns, added patterns, perished patterns, and unexpected patterns. Another work worthy of notice is that proposed in Ref. 40 which presents the patterns of use for additional services that are currently provided to mobile telemunication subscribers. Factor analysis, clustering and quantitative association rules are used to ?nd the service adoption patterns of segmented groups. From the analysis, three categories of users are identi?ed. The ?rst group consists of a new generation of customers who utilize chargeable additional services using the “direct button”, for leisure and entertainment. The younger generation use their mobile phones more frequently than the older generation, and tend to display higher usage patterns for a variety of additional services. The second group utilizes practical additional services that are lowpriced or free such as “data service” and “phonetophone service” via “Caller ID request service”. The customers in the ?nal 7 group are people who have no general usage characteristics. This study utilizes the association rules found in each cluster to provide strategic guidance to enhance the mobile service market of the corresponding group. The model in Ref. 29 mines customer behavior to assist managers in developing better promotion and other relevant policies for a ?rm. The association rules of the relational database design are implemented in the mining system which provides electronic catalog designs and promotional policies. The association rules from relational database design are utilized to mine consumer behavior in order to generate crossselling proposals for an electronic catalog design and marketing for a retailing mall. In this paper, we propose an approach to develop automatically customer pro?les (said also models) from business data. It involves three steps. In the ?rst step, we use fuzzy clustering to categorize customers. A key feature of this fuzzy clustering model is that the number of groups is determined automatically from data using the partition entropy as a validity measure. In the second step, the dimension (or number of attributes) for each cluster (or group of customers) is reduced by selecting only the most informative attributes. Selection is based on the information loss of an attribute。 while the last section o?ers concluding remarks and shades light on future research. Our Proposal Our approach is threestep and is summar