Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Experimental comparison of human and machine learning formalisms
Proceedings of the sixth international workshop on Machine learning
Approximate inference of functional dependencies from relations
ICDT '92 Selected papers of the fourth international conference on Database theory
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Inductive logic programming with large-scale unstructured data
Machine intelligence 14
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
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
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Distance based approaches to relational learning and clustering
Relational Data Mining
Functional and embedded dependency inference: a data mining point of view
Information Systems - Special issue on Databases: creation, management and utilization
ECML '93 Proceedings of the European Conference on Machine Learning
Fast Outlier Detection in High Dimensional Spaces
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Outlier Mining in Large High-Dimensional Data Sets
IEEE Transactions on Knowledge and Data Engineering
Distance-Based Detection and Prediction of Outliers
IEEE Transactions on Knowledge and Data Engineering
Outlier detection by logic programming
ACM Transactions on Computational Logic (TOCL)
Angle-based outlier detection in high-dimensional data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier detection using default reasoning
Artificial Intelligence
DOLPHIN: An efficient algorithm for mining distance-based outliers in very large datasets
ACM Transactions on Knowledge Discovery from Data (TKDD)
ACM Computing Surveys (CSUR)
Outlier Detection Using Inductive Logic Programming
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Isolation-Based Anomaly Detection
ACM Transactions on Knowledge Discovery from Data (TKDD)
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We present a novel definition of outlier whose aim is to embed an available domain knowledge in the process of discovering outliers. Specifically, given a background knowledge, encoded by means of a set of first-order rules, and a set of positive and negative examples, our approach aims at singling out the examples showing abnormal behavior. The technique here proposed is unsupervised, since there are no examples of normal or abnormal behavior, even if it has connections with supervised learning, since it is based on induction from examples. We provide a notion of compliance of a set of facts with respect to a background knowledge and a set of examples, which is exploited to detect the examples that prevent to improve generalization of the induced hypothesis. By testing compliance with respect to both the direct and the dual concept, we are able to distinguish among three kinds of abnormalities, that are irregular, anomalous, and outlier observations. This allows us to provide a finer characterization of the anomaly at hand and to single out subtle forms of anomalies. Moreover, we are also able to provide explanations for the abnormality of an observation which make intelligible the motivation underlying its exceptionality. We present both exact and approximate algorithms for mining abnormalities. The approximate algorithms improve execution time while guaranteeing good accuracy. Moreover, we discuss peculiarities of the novel approach, present examples of knowledge mined, analyze the scalability of the algorithms, and provide comparison with noise handling mechanisms and some alternative approaches.