Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Feature Subset Selection in Text-Learning
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Association Rule Extraction for Text Mining
FQAS '02 Proceedings of the 5th International Conference on Flexible Query Answering Systems
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Machine Learning and Its Applications, Advanced Lectures
A New Framework to Assess Association Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
Automatic acquisition of a large subcategorization dictionary from corpora
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Mining soft-matching rules from textual data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
Association Rule Extraction for Text Mining
FQAS '02 Proceedings of the 5th International Conference on Flexible Query Answering Systems
Web Usage Mining Via Fuzzy Logic Techniques
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Discovering Synonyms Based on Frequent Termsets
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Feature Selection Method Combined Optimized Document Frequency with Improved RBF Network
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Facilitating design learning through faceted classification of in-service information
Advanced Engineering Informatics
Mining fuzzy frequent itemsets for hierarchical document clustering
Information Processing and Management: an International Journal
Analysis of log files applying mining techniques and fuzzy logic
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
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Text mining is an increasingly important research field because of the necessity of obtaining knowledge from the enormous number of text documents available, especially on the Web. Text mining and data mining, both included in the field of information mining, are similar in some sense, and thus it may seem that data mining techniques may be adapted in a straightforward way to mine text. However, data mining deals with structured data, whereas text presents special characteristics and is basically unstructured. In this context, the aims of this paper are three: - To study particular features of text. - To identify the patterns we may look for in text. - To discuss the tools we may use for that purpose.In relation with the third point we overview existing proposals, as well as some new tools we are developing by adapting data mining tools previously developed by our research group.