LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Finding Intensional Knowledge of Distance-Based Outliers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Granular computing: an emerging paradigm
Granular computing: an emerging paradigm
Constructing rough mereological granules of classifying rules and classifying algorithms
Technologies for constructing intelligent systems
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
Outliers and data mining: finding exceptions in data
Outliers and data mining: finding exceptions in data
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Discussion: From imprecise to granular probabilities
Fuzzy Sets and Systems
Rough sets in perception-based computing
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Eliciting domain knowledge in handwritten digit recognition
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Outlier detection using rough set theory
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Outlier Detection by Interaction with Domain Experts
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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Outliers, defined as data samples markedly different from the rest of their kind, play an important role in modern pattern recognition and data analysis systems. Outlier treatment usually invokes reasoning about the unknown (irregular) using concepts and features pertaining to the known (regular) samples, naturally requires tools for handling uncertainty or ambiguity, incorporates multi-layered approximate reasoning structures, and often relies on an external background knowledge source. Granular Computing and Rough Set theories provide excellent methods and frameworks for such tasks. In this article, we discuss methods for the detection and evaluation of outliers, as well as how to elicit background domain knowledge from outliers using multi-level approximate reasoning schemes.