2014 ClusteringandProjectedClusterin

• (Nie et al., 2014) ⇒ Feiping Nie, Xiaoqian Wang, and Heng Huang. (2014). “Clustering and Projected Clustering with Adaptive Neighbors.” 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:10.1145/2623330.2623726

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Cited By

• http://scholar.google.com/scholar?q=%222014%22+Clustering+and+Projected+Clustering+with+Adaptive+Neighbors
• http://dl.acm.org/citation.cfm?id=2623330.2623726&preflayout=flat#citedby

Quotes

Author Keywords

• Adaptive neighbors; block diagonal similarity matrix; clustering; clustering with dimensionality reduction; data mining

Abstract

Many clustering methods partition the data groups based on the input data similarity matrix. Thus, the clustering results highly depend on the data similarity learning. Because the similarity measurement and data clustering are often conducted in two separated steps, the learned data similarity may not be the optimal one for data clustering and lead to the suboptimal results. In this paper, we propose a novel clustering model to learn the data similarity matrix and clustering structure simultaneously. Our new model learns the data similarity matrix by assigning the adaptive and optimal neighbors for each data point based on the local distances. Meanwhile, the new rank constraint is imposed to the Laplacian matrix of the data similarity matrix, such that the connected components in the resulted similarity matrix are exactly equal to the cluster number. We derive an efficient algorithm to optimize the proposed challenging problem, and show the theoretical analysis on the connections between our method and the K - means clustering, and spectral clustering. We also further extend the new clustering model for the projected clustering to handle the high-dimensional data. Extensive empirical results on both synthetic data and real-world benchmark data sets show that our new clustering methods consistently outperforms the related clustering approaches.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ClusteringandProjectedClusterin	Feiping Nie Heng Huang Xiaoqian Wang			Clustering and Projected Clustering with Adaptive Neighbors				10.1145/2623330.2623726		2014