Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Outlier-robust clustering using independent components
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Outlier Detection: An Approximate Reasoning Approach
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Gaining insight in domestic violence with Emergent Self Organizing Maps
Expert Systems with Applications: An International Journal
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Wisdom Technology: A Rough-Granular Approach
Aspects of Natural Language Processing
Information systems in modeling interactive computations on granules
Theoretical Computer Science
A concept discovery approach for fighting human trafficking and forced prostitution
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
Interactive information systems: Toward perception based computing
Theoretical Computer Science
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We present a method for improving the detection of outlying Fire Service's reports based on domain knowledge and dialogue with Fire & Rescue domain experts. The outlying report is considered as an element which is significantly different from the remaining data. We follow the position of Professor Andrzej Skowron that effective algorithms in data mining and knowledge discovery in big data should incorporate an interaction with domain experts or/and be domain oriented. Outliers are defined and searched on the basis of domain knowledge and dialogue with experts. We face the problem of reducing high data dimensionality without loosing specificity and real complexity of reported incidents. We solve this problem by introducing a knowledge based generalization level intermediating between analyzed data and experts domain knowledge. In our approach we use the Formal Concept Analysis methods for both generation of the appropriate categories from data and as tools supporting communication with domain experts. We conducted two experiments in finding two types of outliers in which outlier detection was supported by domain experts.