免费文档-毕业设计-软件工程-外文翻译-客户服务支持原文-2万字符不计空格中文译文：http221122132136p-63076598html

免费文档-毕业设计-软件工程-外文翻译-客户服务支持原文-2万字符不计空格中文译文：http221122132136p-63076598html内容摘要：

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。 others may require more than one. In this research, Database Miner was chosen. This system was developed by the Database Miner Research Group from the Intelligent Database Systems Research Laboratory at Simon Fraser University in Canada. The system, which integrates data warehousing, online analytical processing (OLAP) and data mining techniques, supports the discovery of various kinds of knowledge at multiple conceptual levels from large relational databases. The Database Miner system supports most of the major functions. It was implemented using many advanced data mining techniques. In addition, it provides multidimensional data visualization support and interacts with standard data sources through open database connectivity (ODBC) interface. 、 Mining unstructured data Although Database Miner is an excellent data mining tool for large databases with structured data, it is unsuitable for extracting knowledge from the textual data of the customer service database. As the information or knowledge on mon faults and their suggested remedies are stored in textual format as fault conditions and checkpoints, new techniques are needed to extract knowledge from this database for machine fault diagnosis. This is known as text mining. Traditionally, casebased reasoning (CBR) has been successfully applied to fault diagnosis for customer service support . CBR systems rely on building a large repository of diagnostic cases (or past service reports) in order to circumvent the difficult task of extracting and encoding expert domain knowledge . It is one of the most appropriate techniques for machine fault diagnosis, as it learns by experience gained in solving problems and hence emulates humanlike intelligence. However, the performance of CBR systems critically depends on the adequacy as well as the anization of cases and the algorithms used for retrieval from a large case database. Most CBR systems use the nearest neighbor algorithm for retrieval from the 175。 atindexed case database。 this is inefficient, especially for large case database. Other CBR systems use hierarchical indexing such as CART , decision trees , and although this performs efficient retrieval, building a hierarchical index needs the knowledge of an expert during the caseauthoring phase. The neural work approach provides an efficient learning capability when provided detailed examples. Neural works may be either supervised or unsupervised, depending on the method of training. It performs retrieval based on nearest neighbor matching, since it stores the weight vectors as the codebook or exemplar vector for the input patterns. The matching is based on a petitive process that determines the output unit that is the best match for the input vector, similar to the nearest neighbor rule. However, the search space in a neural work is greatly reduced because of the generalizations of knowledge through training. In contrast, the CBR systems need to store all the cases in the case database in order to perform accurate retrieval. The CBR systems that store only relevant cases for an efficient retrieval lack the accuracy as well as the learning feature. Thus, neural works are very suitable for case indexing and retrieval. Other data mining techniques include rulebased reasoning, fuzzy logic, geic algorithms, decision trees, inductive learning systems, and statistical pattern classification systems. In addition, hybrid approaches, such as hybrid casebased reasoning and neural work , have also been developed. Here, a data mining technique that integrates case based reasoning, neural work and rulebased reasoning is defined. These two are incorporated into the fra。