预处理：发现WUM过程模式的前提
Preprocessing: A Prerequisite for Discovering Patterns in WUM Process
摘要：
Web日志数据通常是多样的和大量的。这些数据必须被组装成一个一致的、集成的和全面的视图，以便用于模式发现。如果在分析之前没有正确地清理、转换和结构化数据，就不能期望找到有意义的模式。在大多数数据采掘应用中，数据预处理包括删除和过滤冗余和不相关的数据，去除噪声，转换和解决任何不一致。在本文中，一个完整的预处理方法，合并，数据清洗，用户/会话识别和数据格式化和总结活动，以提高数据的质量，减少数据量已被提出。为了验证所提出的预处理方法的效率，进行了几个实验，结果表明，所提出的方法减少了Web访问日志文件的大小下降到初始大小的73-82%，并提供了更丰富的日志结构的Web使用挖掘（WUM）的进一步阶段。因此，WUM过程中的原始数据预处理是本文的中心主题。
英文摘要：
Web log data is usually diverse and voluminous. This data must be assembled into a consistent, integrated and comprehensive view, in order to be used for pattern discovery. Without properly cleaning, transforming and structuring the data prior to the analysis, one cannot expect to find meaningful patterns. As in most data mining applications, data preprocessing involves removing and filtering redundant and irrelevant data, removing noise, transforming and resolving any inconsistencies. In this paper, a complete preprocessing methodology having merging, data cleaning, user/session identification and data formatting and summarization activities to improve the quality of data by reducing the quantity of data has been proposed. To validate the efficiency of the proposed preprocessing methodology, several experiments are conducted and the results show that the proposed methodology reduces the size of Web access log files down to 73-82% of the initial size and offers richer logs that are structured for further stages of Web Usage Mining (WUM). So preprocessing of raw data in this WUM process is the central theme of this paper.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
