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http://dx.doi.org/10.3745/KIPSTD.2007.14-D.2.169

An Unsupervised Clustering Technique of XML Documents based on Function Transform and FFT  

Lee, Ho-Suk (호서대학교 공과대학 뉴미디어학과)
Publication Information
The KIPS Transactions:PartD / v.14D, no.2, 2007 , pp. 169-180 More about this Journal
Abstract
This paper discusses a new unsupervised XML document clustering technique based on the function transform and FFT(Fast Fourier Transform). An XML document is transformed into a discrete function based on the hierarchical nesting structure of the elements. The discrete function is, then, transformed into vectors using FFT. The vectors of two documents are compared using a weighted Euclidean distance metric. If the comparison is lower than the pre specified threshold, the two documents are considered similar in the structure and are grouped into the same cluster. XML clustering can be useful for the storage and searching of XML documents. The experiments were conducted with 800 synthetic documents and also with 520 real documents. The experiments showed that the function transform and FFT are effective for the incremental and unsupervised clustering of XML documents similar in structure.
Keywords
Unsupervised Clustering; Structure of Elements; Function Transform; FFT; Weighted Euclidean Distance;
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