Temporal databases: theory, design, and implementation
Temporal databases: theory, design, and implementation
Approximate inference of functional dependencies from relations
ICDT '92 Selected papers of the fourth international conference on Database theory
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Database Management Systems
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
TAR: Temporal Association Rules on Evolving Numerical Attributes
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Automated Mining of Fuzzy Association Rules
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Outlier Detection and Data Cleaning in Multivariate Non-Normal Samples: The PAELLA Algorithm
Data Mining and Knowledge Discovery
International Journal of Intelligent Systems - Intelligent and Soft Computing Techniques for Information Processing
Linear correlation discovery in databases: a data mining approach
Data & Knowledge Engineering
A Uniform Algebraic Characterization of Temporal Functional Dependencies
TIME '05 Proceedings of the 12th International Symposium on Temporal Representation and Reasoning
SMCA: A General Model for Mining Asynchronous Periodic Patterns in Temporal Databases
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
KDDML: a middleware language and system for knowledge discovery in databases
Data & Knowledge Engineering
Online outlier detection in sensor data using non-parametric models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
ACM Transactions on Database Systems (TODS)
The ramification problem in temporal databases: changing beliefs about the past
Data & Knowledge Engineering - Special issue: WIDM 2004
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Data & Knowledge Engineering
ACM Computing Surveys (CSUR)
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Temporal Outlier Detection in Vehicle Traffic Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
An approach for temporal analysis of email data based on segmentation
Data & Knowledge Engineering
Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
Data & Knowledge Engineering
Fuzzy clustering-based discretization for gene expression classification
Knowledge and Information Systems
A new feature weighted fuzzy clustering algorithm
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Summarizing XML data by means of association rules
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Editorial: New fuzzy c-means clustering model based on the data weighted approach
Data & Knowledge Engineering
Workflow mining and outlier detection from clinical activity logs
Journal of Biomedical Informatics
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The problem of detecting outliers has been investigated in several research areas such as database, machine learning, knowledge discovery, and logic programming, with the aim of identifying objects of a given population whose behavior is different from that of the other data objects of the dataset. Outliers represent semantically correct situations, albeit infrequent with respect to the majority of cases. Detecting them allows extracting useful and actionable knowledge of interest to domain experts. In this paper, we focus our attention on the analysis of outlier detection in temporal databases. We propose a method, based on association rules, to infer the normal behavior of objects by extracting frequent rules from a given dataset. To include the time information, we define the concept of temporal association rules. Then, temporal association rules are combined to generate temporal quasi-functional dependencies, which define relationships among attributes over time which hold frequently. Once such dependencies have been inferred from data, outliers are retrieved with respect to them. Given a temporal quasi-functional dependency, it is possible to discover the outliers by querying the temporal association rules stored previously. Our method is independent of the considered database and infers rules, used to highlight outliers, directly from data. The applicability of the proposed approach is validated through a set of experiments which show its effectiveness and efficiency.