The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
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
Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Reasoning for web document associations and its applications in site map construction
Data & Knowledge Engineering
IEEE Intelligent Systems
OPTICS-OF: Identifying Local Outliers
PKDD '99 Proceedings of the Third 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
Principles of human-computer collaboration for knowledge discovery in science
Artificial Intelligence
ACM SIGKDD Explorations Newsletter
Ranking Complex Relationships on the Semantic Web
IEEE Internet Computing
The case for anomalous link detection
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Social Network Discovery by Mining Spatio-Temporal Events
Computational & Mathematical Organization Theory
The case for anomalous link discovery
ACM SIGKDD Explorations Newsletter
Discovering informative connection subgraphs in multi-relational graphs
ACM SIGKDD Explorations Newsletter
Graph building as a mining activity: finding links in the small
Proceedings of the 3rd international workshop on Link discovery
Processing-in-memory technology for knowledge discovery algorithms
DaMoN '06 Proceedings of the 2nd international workshop on Data management on new hardware
ACM Transactions on Database Systems (TODS)
Finding tribes: identifying close-knit individuals from employment patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Anomaly detection in data represented as graphs
Intelligent Data Analysis
The Most Reliable Subgraph Problem
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Interesting instance discovery in multi-relational data
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Finding the k-Most Abnormal Subgraphs from a Single Graph
DS '09 Proceedings of the 12th International Conference on Discovery Science
STEvent: Spatio-temporal event model for social network discovery
ACM Transactions on Information Systems (TOIS)
Derived types in semantic association discovery
Journal of Intelligent Information Systems
Link discovery in graphs derived from biological databases
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
Link analysis tools for intelligence and counterterrorism
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
SigSpot: mining significant anomalous regions from time-evolving networks (abstract only)
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Ranking semantic associations between two entities --- extended model
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Review of bisonet abstraction techniques
Bisociative Knowledge Discovery
Efficient Identification of Linchpin Vertices in Dependence Clusters
ACM Transactions on Programming Languages and Systems (TOPLAS)
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A significant portion of knowledge discovery and datamining research focuses on finding patterns of interest indata. Once a pattern is found, it can be used to recognizesatisfying instances. The new area of link discoveryrequires a complementary approach, since patterns ofinterest might not yet be known or might have too fewexamples to be learnable. This paper presents anunsupervised link discovery method aimed at discoveringunusual, interestingly linked entities in multi-relationaldatasets. Various notions of rarity are introduced tomeasure the "interestingness" of sets of paths andentities. These measurements have been implemented andapplied to a real-world bibliographic dataset where theygive very promising results.