NDLI: Clustering XML Documents Based on Data Type

Content Provider	IEEE Xplore Digital Library
Author	Chong Zhou Yansheng Lu
Copyright Year	2008
Description	Author affiliation: Coll. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., China (Chong Zhou; Yansheng Lu)
Abstract	The existing so-called semantic XML document clustering algorithms usually use a synonymous word library to calculate semantic similarities among XML documents. However, when people create their own XML documents, they name the element randomly and often use lots of abbreviations. Many tags are not real words at all. The XML documents created by different people may appear very different from each other even if they describe the same object. The traditional methods do not work well in such case. To address the problem, we proposed a novel similarity measure standard based on data-type tree, a model integrating data types and tags of XML documents. A clustering algorithm $DT^{2}K-means$ is also proposed to cluster XML documents. Empirical experiment results on real world data sets show $DT^{2}K-means$ can group the semantic similar XML documents together correctly, which contain different tags but describe the same object.
Starting Page	122
Ending Page	127
File Size	268982
Page Count	6
File Format	PDF
ISBN	9780769535081
DOI	10.1109/CIS.2008.90
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Stability Data security Measurement standards XML Clustering algorithms Educational institutions Libraries clustering Computer security Computational intelligence data type
Content Type	Text
Resource Type	Article

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