2014 BatchDiscoveryofRecurringRareCl

• (Dundar et al., 2014) ⇒ Murat Dundar, Halid Ziya Yerebakan, and Bartek Rajwa. (2014). “Batch Discovery of Recurring Rare Classes Toward Identifying Anomalous Samples.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623695

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Batch+Discovery+of+Recurring+Rare+Classes+Toward+Identifying+Anomalous+Samples
• http://dl.acm.org/citation.cfm?id=2623330.2623695&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; anomaly detection; batch clustering; [[hierarchical dirichlet process; medical information systems; random effects; rare classes; recurring classes

Abstract

We present a clustering algorithm for discovering rare yet significant recurring classes across a batch of samples in the presence of random effects. We model each sample data by an infinite mixture of Dirichlet-process Gaussian-mixture models (DPMs) with each DPM representing the noisy realization of its corresponding class distribution in a given sample. We introduce dependencies across multiple samples by placing a global Dirichlet process prior over individual DPMs. This hierarchical prior introduces a sharing mechanism across samples and allows for identifying local realizations of classes across samples. We use collapsed Gibbs sampler for inference to recover local DPMs and identify their class associations. We demonstrate the utility of the proposed algorithm, processing a flow cytometry data set containing two extremely rare cell populations, and report results that significantly outperform competing techniques. The source code of the proposed algorithm is available on the web via the link:http://cs.iupui.edu/~dundar / aspire.htm.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 BatchDiscoveryofRecurringRareCl	Bartek Rajwa Murat Dundar Halid Ziya Yerebakan			Batch Discovery of Recurring Rare Classes Toward Identifying Anomalous Samples				10.1145/2623330.2623695		2014