【正文】 the users can select different data mining tools for their problem domains to achieve the best results. The choice of data mining tool must be based on the application domain and its supported purpose of this experiment was to test the performance when the input was less precise in describing the fault condition. Unlike the service records tested in the first experiment, user input was less accurate than that of service engineers. In this test, all the four retrieval techniques were found to have lower retrieval accuracy due to the impreciseness and grammatical variation of the user input. On the other hand, the retrieval accuracy of the nearest neighbor and fuzzy trigram matching techniques were 72 and 76%, respectively. Fuzzytrigram matching has a better performance than the Euclidean distance matching because of its ability to handle spelling mistakes and grammatical variations in the user input. However, its retrieval accuracy is lower than that of the two neural works. Certain applications may require only one data mining function。 Decision support。s problem description as input, maps the description into the closest faultconditions of the faults previously stored from the knowledge base, and retrieves the corresponding checkpoint solutions for the user. The user39。 Knowledge discovery in databases。atindexed case database。 ., more than one data mining techniques. For example, Darwin from Thinking Machine Corp. supports neural works, regression tree (CART), kmeans algorithm, and case based reasoning for classification, prediction, and clustering functions. There are also some tools that only aim at a special data mining function. This provides 175。s premise for an onsite repair. During such trips, the service engineer will take past records of the customer39。 then improve the design and reliability of those machine models in order to increase sales. Target the customers with mail campaigns of the machine models in which they are likely to be interested. _ Customer support: provide the best possible service to customers based on the machine model, the nature of the problem, and geographical location. _ Resource management: assign duties to service engineers based on their expertise and past experience. Promote service engineers based on their performance. 、 Selection of data This step identifies a subset of variables or data samples, on which mining can be performed. There are many tables in the database. However, not all are suitable for mining, since they are not sufficiently large. After an initial study, the structured data tables EMPLOYEE and CUSTOMER were found unsuitable for mining, while MACHINE and SERVICE_REPORT were considered suitable for mining. 、 Data preprocessing This step removes the noisy, erroneous, and inplete data. The presence of too many different categories of categorical data makes visualization of the displayed information very difficult. Hence, those categories with only a few records are eliminated. Moreover, all the records with missing values are deleted to avoid problems in visualization. Since the proportion of such records is quite small, their deletion will have little effect on the results. 、 Data transformation The data stored in the various tables are in a specified format (defined during the construction of the database). Sometimes, it is useful to transform the data into a new format in order to mine additional information. For example, a new column svc_repair_time (service repair time) is created by calculating the difference, measured in number of days, between svc_start_dt and svc_end_dt in the SERVICE_REPORT table. This new attribute is useful in analyzing the performance of the service engineers. 、 Data warehousing Data warehousing is the process of visioning, planning, building, using, managing, maintaining and enhancing databases. The data suitable for mining are collected from the various tables of the customer service database and stored in DB Miner39。 Data mining for customer service support Abstract In traditional customer service support of a manufacturing environment, a customer service database usually stores two types of service information: unstructured customer service reports record machine problems and its remedial actions and structured data on sales, employees, and customers for daytoday management operations. This paper investigates how to apply data mining techniques to extract knowledge from the database to support two kinds of customer service activities: decision support and machine fault diagnosis. A data mining process, based on the data mining tool Database Miner, was investigated to provide structured man