BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of fraud rules for telecommunications—challenges and solutions
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine learning and data mining
Communications of the ACM
ACM Computing Surveys (CSUR)
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
Support Vector Machines for Knowledge Discovery
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
Finding Intensional Knowledge of Distance-Based Outliers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A boosting approach to remove class label noise
International Journal of Hybrid Intelligent Systems - Hybrid Intelligent systems in Ensembles
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In this paper, we apply data squashing to speed up outlier detection based on boosting. One person's noise is another person's signal. Outlier detection is gaining increasing attention in data mining. In order to improve computational time for AdaBoost-based outlier detection, we beforehand compress a given data set based on a simplified method of BIRCH. Effectiveness of our approach in terms of detection accuracy and computational time is investigated by experiments with two real-world data sets of drug stores in Japan and an artificial data set of unlawful access to a computer network.