Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Data mining: concepts and techniques
Data mining: concepts and techniques
Mining top-n local outliers in large databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Graph Algorithms
Outlier detection and evaluation by network flow
International Journal of Computer Applications in Technology
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Detecting outliers is an important topic in data mining. Sometimes the outliers are more interesting than the rest of the data. Outlier identification has lots of applications, such as intrusion detection, and unusual usage of credit cards or telecommunication services. In this paper, we propose a novel method for outlier identification which is based on network flow. We use the well known Maximum Flow Minimum Cut theorem from graph theory to find the outliers and strong outlier groups. Especially, it works on high dimensional data. This outlier detection occurs in a novel setting: to repair poor quality clusters generated by a clustering algorithm.