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» Distributed Data Clustering Can Be Efficient and Exact
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IDA
2007
Springer
16 years 27 days ago
Compact and Understandable Descriptions of Mixtures of Bernoulli Distributions
Abstract. Finite mixture models can be used in estimating complex, unknown probability distributions and also in clustering data. The parameters of the models form a complex repres...
Jaakko Hollmén, Jarkko Tikka
CCGRID
2010
IEEE
15 years 4 months ago
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds
Data-intensive parallel applications on clouds need to deploy large data sets from the cloud's storage facility to all compute nodes as fast as possible. Many multicast algori...
Tatsuhiro Chiba, Mathijs den Burger, Thilo Kielman...
CLUSTER
2006
IEEE
15 years 6 months ago
A flexible multi-dimensional QoS performance measure framework for distributed heterogeneous systems
When users' tasks in a distributed heterogeneous computing environment (e.g., cluster of heterogeneous computers) are allocated resources, the total demand placed on some sys...
Jong-Kook Kim, Debra A. Hensgen, Taylor Kidd, Howa...
SDM
2008
SIAM
139views Data Mining» more  SDM 2008»
15 years 8 months ago
Simultaneous Unsupervised Learning of Disparate Clusterings
Most clustering algorithms produce a single clustering for a given data set even when the data can be clustered naturally in multiple ways. In this paper, we address the difficult...
Prateek Jain, Raghu Meka, Inderjit S. Dhillon
ICCV
2009
IEEE
1556views Computer Vision» more  ICCV 2009»
16 years 11 months ago
Kernel Methods for Weakly Supervised Mean Shift Clustering
Mean shift clustering is a powerful unsupervised data analysis technique which does not require prior knowledge of the number of clusters, and does not constrain the shape of th...
Oncel Tuzel, Fatih Porikli, Peter Meer