On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Data mining: concepts and techniques
Data mining: concepts and techniques
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Text Mining at Detail Level Using Conceptual Graphs
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
Conceptual Graph Interchange Format for Mining Financial Statements
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
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Deviation detection is an important problem of both data and text mining. In this paper we consider the detection of deviations in a set of texts represented as conceptual graphs. In contrast with statistical and distance-based approaches, the method we propose is based on the concept of generalization and regularity. Among its main characteristics are the detection of rare patterns (that attempt to give a generalized description of rare texts) and the ability to discover local deviations (deviations at different contexts and generalization levels). The method is illustrated with the analysis of a set of computer science papers.