Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Static and dynamic information organization with star clusters
Proceedings of the seventh international conference on Information and knowledge management
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
A vector space model for automatic indexing
Communications of the ACM
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Survey of Text Mining
Entity-based cross-document coreferencing using the Vector Space Model
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Efficient Phrase-Based Document Indexing for Web Document Clustering
IEEE Transactions on Knowledge and Data Engineering
Model-based overlapping clustering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
The Evaluation Measure of Text Clustering for the Variable Number of Clusters
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A Clustering Algorithm Based on Generalized Stars
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
A Fast Algorithm to Find Overlapping Communities in Networks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Characterization and evaluation of similarity measures for pairs of clusterings
Knowledge and Information Systems
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
A New Incremental Algorithm for Overlapped Clustering
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Dynamic hierarchical algorithms for document clustering
Pattern Recognition Letters
SSDE: fast graph drawing using sampled spectral distance embedding
GD'06 Proceedings of the 14th international conference on Graph drawing
ACONS: a new algorithm for clustering documents
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Finding Overlapping Communities in Social Networks
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Neurocomputing
SimClus: an effective algorithm for clustering with a lower bound on similarity
Knowledge and Information Systems - Special Issue on Data Warehousing and Knowledge Discovery from Sensors and Streams
Efficient identification of overlapping communities
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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Clustering is a Data Mining technique, which has been widely used in many practical applications. From these applications, there are some, like social network analysis, topic detection and tracking, information retrieval, categorization of digital libraries, among others, where objects may belong to more than one cluster; however, most clustering algorithms build disjoint clusters. In this work, we introduce OClustR, a new graph-based clustering algorithm for building overlapping clusters. The proposed algorithm introduces a new graph-covering strategy and a new filtering strategy, which together allow to build overlapping clusterings more accurately than those built by previous algorithms. The experimental evaluation, conducted over several standard collections, showed that our proposed algorithm builds less clusters than those built by the previous related algorithms. Additionally, OClustR builds clusters with overlapping closer to the real overlapping in the collections than the overlapping generated by other clustering algorithms.