Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A tree-based approach to clustering XML documents by structure
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Content and Structure Based Approach For XML Similarity
CIT '05 Proceedings of the The Fifth International Conference on Computer and Information Technology
A methodology for clustering XML documents by structure
Information Systems
XCLS: a fast and effective clustering algorithm for heterogenous XML documents
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
XML Filtering Using Dynamic Hierarchical Clustering of User Profiles
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Utilizing XML Clustering for Efficient XML Data Management on P2P Networks
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
A weighted common structure based clustering technique for XML documents
Journal of Systems and Software
XML data clustering: An overview
ACM Computing Surveys (CSUR)
Collaborative clustering of XML documents
Journal of Computer and System Sciences
Exploring dictionary-based semantic relatedness in labeled tree data
Information Sciences: an International Journal
Hierarchical clustering of XML documents focused on structural components
Data & Knowledge Engineering
Structural and semantic similarity for XML comparison
Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
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In this paper we propose a unified clustering algorithm for both homogeneous and heterogeneous XML documents. Depending on the type of the XML documents, the proposed algorithm modifies its distance metric in order to properly adapt to the special structural characteristics of homogeneous and heterogeneous XML documents. We compare the quality of the formed clusters with those of one of the latest XML clustering algorithms and show that our algorithm outperforms it in the case of both homogeneous and heterogeneous XML documents.