Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Web Document Classification Based on Fuzzy Association
COMPSAC '02 Proceedings of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopment
Granular computing using information tables
Data mining, rough sets and granular computing
Similarity between words computed by spreading activation on an English dictionary
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
The computation of word associations: comparing syntagmatic and paradigmatic approaches
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A Ten-year Review of Granular Computing
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Inter and intra-document contexts applied in polyrepresentation for best match IR
Information Processing and Management: an International Journal
Granular Computing and Modeling the Human Thoughts in Web Documents
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Proceedings of the Third international conference on Formal Concept Analysis
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
New spectral methods for ratio cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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The amount of data available in semi-structured or unstructured format grows exponentially. The area of text mining aims at discovering knowledge from data of this type. Most work in this area uses the model known as bag of words to represent the texts. This form of representation, although effective, minimizes the quality of knowledge discovered because it is not able to capture essential characteristics of this type of data such as semantics and context. The paradigm of granular computing has been shown effective in the treatment of complex problems of information processing and can produce significant results in large-scale environments such as the Web. This paper explores the granulation process of words with a view to its application in the subsequent improvement in text representation. We use fuzzy relations and spectral clustering in this process and present some results.