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SDM
2012
SIAM

452views Data Mining» more SDM 2012»

Density-based Projected Clustering over High Dimensional Data Streams

12 years 1 months ago

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Clustering of high dimensional data streams is an important problem in many application domains, a prominent example being network monitoring. Several approaches have been lately ...

Irene Ntoutsi, Arthur Zimek, Themis Palpanas, Peer...

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Id	Views	Title	Status

1	452	Density-based Projected Clustering over High Dimensional Data Streams	claim
2	355	Granger Causality Analysis in Irregular Time Series	claim
3	340	IntruMine: Mining Intruders in Untrustworthy Data of Cyber-physical Systems	claim
4	322	Adaptive Multi-task Sparse Learning with an Application to fMRI Study	claim
5	312	Drought Detection of the Last Century: An MRF-based Approach	claim
6	307	Pseudo Cold Start Link Prediction with Multiple Sources in Social Networks	claim
7	305	Learning Hierarchical Relationships among Partially Ordered Objects with Heterogeneous Attributes and Links	claim
8	304	Robust Reputation-Based Ranking on Bipartite Rating Networks	claim
9	304	Fast Random Walk Graph Kernel	claim
10	297	A Flexible Open-Source Toolbox for Scalable Complex Graph Analysis	claim
11	294	Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information	claim
12	293	RP-growth: Top-k Mining of Relevant Patterns with Minimum Support Raising	claim
13	289	Mining Compressing Sequential Patterns	claim
14	285	A Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets	claim
15	284	The Similarity Between Stochastic Kronecker and Chung-Lu Graph Models	claim
16	282	Citation Prediction in Heterogeneous Bibliographic Networks	claim
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18	278	Legislative Prediction via Random Walks over a Heterogeneous Graph	claim
19	273	A Framework for the Evaluation and Management of Network Centrality	claim
20	261	Combining Active Learning and Dynamic Dimensionality Reduction	claim
21	258	Feature Selection with Linked Data in Social Media	claim
22	252	Learning from Heterogeneous Sources via Gradient Boosting Consensus	claim
23	247	Image Mining of Historical Manuscripts to Establish Provenance	claim
24	247	Simplex Distributions for Embedding Data Matrices over Time	claim
25	245	Deterministic CUR for Improved Large-Scale Data Analysis: An Empirical Study	claim
26	238	Evaluating Event Credibility on Twitter	claim
27	237	A Distributed Kernel Summation Framework for General-Dimension Machine Learning	claim
28	235	Sampling Strategies to Evaluate the Performance of Unknown Predictors	claim
29	234	Monitoring and Mining Insect Sounds in Visual Space	claim
30	234	On Evaluation of Outlier Rankings and Outlier Scores	claim
31	233	On Finding Joint Subspace Boolean Matrix Factorizations	claim
32	224	Nearest-Neighbor Search on a Time Budget via Max-Margin Trees	claim
33	220	Transfer Topic Modeling with Ease and Scalability	claim
34	216	Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable	claim
35	208	Mining Massive Archives of Mice Sounds with Symbolized Representations	claim

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