Proceedings of the 2016 SIAM International Conference on Data Mining
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Published By Society For Industrial And Applied Mathematics

9781611974348

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Kernelized Matrix Factorization for Collaborative Filtering

Proceedings of the 2016 SIAM International Conference on Data Mining ◽

10.1137/1.9781611974348.43 ◽

2016 ◽

Cited By ~ 20

Author(s):

Xinyue Liu ◽

Chara Aggarwal ◽

Yu-Feng Li ◽

Xiaugnan Kong ◽

Xinyuan Sun ◽

...

Keyword(s):

Collaborative Filtering ◽

Matrix Factorization

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Automatic Unsupervised Tensor Mining with Quality Assessment

Proceedings of the 2016 SIAM International Conference on Data Mining ◽

10.1137/1.9781611974348.80 ◽

2016 ◽

Cited By ~ 16

Author(s):

Evangelos E. Papalexakis

Keyword(s):

Quality Assessment

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BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

Proceedings of the 2016 SIAM International Conference on Data Mining ◽

10.1137/1.9781611974348.56 ◽

2016 ◽

Cited By ~ 28

Author(s):

Bryan Hooi ◽

Neil Shah ◽

Alex Beutel ◽

Stephan Günnemann ◽

Leman Akoglu ◽

...

Keyword(s):

Bayesian Inference ◽

Fraud Detection

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Copula-HDP-HMM: Non-parametric Modeling of Temporal Multivariate Data for I/O Efficient Bulk Cache Preloading

Proceedings of the 2016 SIAM International Conference on Data Mining ◽

10.1137/1.9781611974348.87 ◽

2016 ◽

Author(s):

Lavanya Sita Tekumalla ◽

Chiranjib Bhattacharyya

Keyword(s):

Multivariate Data ◽

Parametric Modeling ◽

Non Parametric

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Top-N Recommendation with Novel Rank Approximation

Proceedings of the 2016 SIAM International Conference on Data Mining ◽

10.1137/1.9781611974348.15 ◽

2016 ◽

Cited By ~ 4

Author(s):

Zhao Kang ◽

Qiang Cheng

Keyword(s):

Rank Approximation

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Front Matter

Proceedings of the 2016 SIAM International Conference on Data Mining ◽

10.1137/1.9781611974348.fm ◽

2016 ◽

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Geometric methods to accelerate k-means algorithms

Proceedings of the 2016 SIAM International Conference on Data Mining ◽

10.1137/1.9781611974348.37 ◽

2016 ◽

Cited By ~ 2

Author(s):

Petr Ryšavý ◽

Greg Hamerly

Keyword(s):

Geometric Methods

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Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice

Proceedings of the 2016 SIAM International Conference on Data Mining ◽

10.1137/1.9781611974348.54 ◽

2016 ◽

Cited By ~ 2

Author(s):

Xide Xia ◽

Pavlos Protopapas ◽

Finale Doshi-Velez

Keyword(s):

Active Learning ◽

Astronomical Observation ◽

Batch Mode

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A Spatial-Temporal Probabilistic Matrix Factorization Model for Point-of-Interest Recommendation

Proceedings of the 2016 SIAM International Conference on Data Mining ◽

10.1137/1.9781611974348.14 ◽

2016 ◽

Cited By ~ 9

Author(s):

Huayu Li ◽

Richang Hong ◽

Zhiang Wu ◽

Yong Ge

Keyword(s):

Matrix Factorization ◽

Point Of Interest ◽

Factorization Model ◽

Probabilistic Matrix Factorization

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A general framework to increase the robustness of model-based change point detection algorithms to outliers and noise

Proceedings of the 2016 SIAM International Conference on Data Mining ◽

10.1137/1.9781611974348.19 ◽

2016 ◽

Author(s):

Xi C. Chen ◽

Yuanshun Yao ◽

Sichao Shi ◽

Snigdhansu Chatterjee ◽

Vipin Kumar ◽

...

Keyword(s):

Change Point ◽

General Framework ◽

Change Point Detection ◽

Detection Algorithms ◽

Model Based ◽

Point Detection

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Proceedings of the 2016 SIAM International Conference on Data MiningLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Society For Industrial And Applied Mathematics

Kernelized Matrix Factorization for Collaborative Filtering

Automatic Unsupervised Tensor Mining with Quality Assessment

BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

Copula-HDP-HMM: Non-parametric Modeling of Temporal Multivariate Data for I/O Efficient Bulk Cache Preloading

Top-N Recommendation with Novel Rank Approximation

Front Matter

Geometric methods to accelerate k-means algorithms

Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice

A Spatial-Temporal Probabilistic Matrix Factorization Model for Point-of-Interest Recommendation

A general framework to increase the robustness of model-based change point detection algorithms to outliers and noise

Proceedings of the 2016 SIAM International Conference on Data Mining
Latest Publications