Clustering Workflow Requirements Using Compression Dissimilarity Measure

Li Wei, J. Handley, Nathaniel Martin, Tong Sun, Eamonn J. Keogh
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引用次数: 8

Abstract

Xerox offers a bewildering array of printers and software configurations to satisfy the need of production print shops. A configuration tool in the hands of sales analysts elicits requirements from customers and recommends a list of product configurations. This tool generates special question and answer case logs that provide useful historical data. Given the unusual semi-structured question and answer format, this data is not amenable to any standard document clustering method. The authors discovered that a hierarchical agglomerative approach using a compression-based dissimilarity measure (CDM) provided readily interpretable clusters. The authors compared this method empirically to two reasonable alternatives, latent semantic analysis and probabilistic latent semantic analysis, and conclude that CDM offers an accurate and easily implemented approach to validate and augment our configuration tool
使用压缩不相似度度量聚类工作流需求
施乐提供了一系列令人眼花缭乱的打印机和软件配置,以满足生产打印店的需求。销售分析师手中的配置工具可以从客户那里获取需求,并推荐一系列产品配置。该工具生成特殊的问答案例日志,提供有用的历史数据。考虑到不寻常的半结构化问答格式,该数据不适合任何标准文档聚类方法。作者发现,使用基于压缩的不相似性度量(CDM)的分层凝聚方法提供了易于解释的聚类。作者将该方法与潜在语义分析和概率潜在语义分析两种合理的替代方法进行了经验比较,并得出结论,CDM提供了一种准确且易于实现的方法来验证和增强我们的配置工具
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