2009 UnsupervisedClassSeparatio 1R

• (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

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 a 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 1R	Osmar R. Zaïane Andrew Foss Sandra Zilles			Unsupervised Class Separation of Multivariate Data through Cumulative Variance-based Ranking				10.1109/ICDM.2009.17