2012 DependencyClusteringAcrossMeasu

• (Plant, 2012) ⇒ Claudia Plant. (2012). “Dependency Clustering Across Measurement Scales.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339589

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Dependency+Clustering+Across+Measurement+Scales
• http://dl.acm.org/citation.cfm?id=2339530.2339589&preflayout=flat#citedby

Quotes

Author Keywords

• Clustering; data mining; heterogeneous data; minimum description length

Abstract

How to automatically spot the major trends in large amounts of heterogeneous data? Clustering can help. However, most existing techniques suffer from one or more of the following drawbacks: 1) Many techniques support only one particular data type, most commonly numerical attributes. 2) Other techniques do not support attribute dependencies which are prevalent in real data. 3) Some approaches require input parameters which are difficult to estimate. 4) Most clustering approaches lack in interpretability. To address these challenges, we present the algorithm Scenic for dependency clustering across measurement scales. Our approach seamlessly integrates heterogenous data types measured at different scales, most importantly continuous numerical and discrete categorical data. Scenic clusters by arranging objects and attributes in a cluster-specific low-dimensional space. The embedding serves as a compact cluster model allowing to reconstruct the original heterogenous attributes with high accuracy. Thereby embedding reveals the major cluster-specific mixed-type attribute dependencies. Following the Minimum Description Length (MDL) principle, the cluster-specific embedding serves as a codebook for effective data compression. This compression-based view automatically balances goodness-of-fit and model complexity, making input parameters redundant. Finally, the embedding serves as a visualization enhancing the interpretability of the clustering result. Extensive experiments demonstrate the benefits of Scenic.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 DependencyClusteringAcrossMeasu	Claudia Plant			Dependency Clustering Across Measurement Scales				10.1145/2339530.2339589		2012