DocumentCode :
3259099
Title :
Clustering Workflow Requirements Using Compression Dissimilarity Measure
Author :
Li Wei ; Handley, John ; Martin, Nathaniel ; Tong Sun ; Keogh, Eamonn
Author_Institution :
Dept. of Comput. Sci., California Univ., Riverside, CA
fYear :
2006
fDate :
Dec. 2006
Firstpage :
50
Lastpage :
54
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
Keywords :
configuration management; digital printing; printers; workflow management software; answer case logs; bewildering array; clustering workflow requirements; compression dissimilarity measure; document clustering; hierarchical agglomerative; printers configurations; probabilistic latent semantic analysis; software configurations; unusual semistructured question; Books; Clustering methods; Computer science; Finishing; Marketing and sales; Presses; Printers; Printing; Production; Publishing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2702-7
Type :
conf
DOI :
10.1109/ICDMW.2006.44
Filename :
4063597
Link To Document :
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