2009 UnsupervisedClassSeparatio

• (Foss et al., 2009) ⇒ Andrew Foss, Osmar R. Zaïane, Sandra Zilles. (2009). “Unsupervised Class Separation of Multivariate Data through Cumulative Variance-based Ranking.” In: Proceedings of the Ninth IEEE International Conference on Data Mining (ICDM 2009). doi:10.1109/ICDM.2009.17

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Abstract

• This paper introduces a new extension of outlier detection approaches and a new concept, class separation through variance. We show that accumulating information about the outlierness of points in multiple subspaces leads to an ranking in which classes with differing variance naturally tend to separate. Exploiting this leads to a highly effective and efficient unsupervised class separation approach, especially useful in the difficult case of heavily overlapping distributions. Unlike typical outlier detection algorithms, this method can be applied beyond the ` rare classes ' case with great success. Two novel algorithms that implement this approach are provided. Additionally, experiments show that the novel methods typically outperform other state-of-the-art outlier detection methods on high dimensional data such as Feature Bagging, SOE1, LOF, ORCA and Robust Mahalanobis Distance and competes even with the leading supervised classification methods.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 UnsupervisedClassSeparatio	Osmar R. Zaïane Andrew Foss Sandra Zilles			Unsupervised Class Separation of Multivariate Data through Cumulative Variance-based Ranking		ICDM 2009 Proceedings		10.1109/ICDM.2009.17		2009