'Outlier detection' diaporamas de présentation

Quality Assurance & Quality Control

1.04k views • 64 slides

Data Editing, Coding, and Just a Little Imputation

Data Editing, Coding, and Just a Little Imputation. Katherine (Jenny) Thompson Office of Statistical Methods and Research for Economic Programs Katherine.J.Thompson@census.gov (301) 763-4941. The Basics: What is Editing?.

1.05k views • 55 slides

Surprise Detection in Science Data Streams

Surprise Detection in Science Data Streams. Kirk Borne Dept of Computational & Data Sciences George Mason University. kborne@gmu.edu , http://classweb.gmu.edu/kborne/. Outline. Astroinformatics Example Application: The LSST Project New Algorithm for Surprise Detection: KNN-DD. Outline.

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Summary

Using Selective Editing Combined with an Automatic System in the FSS of Spain Dolores Lorca National Statistical Institute of Spain. Summary. An integrated editing process that combines selective editing and the generalized edit and imputation system Banff

568 views • 42 slides

Manish Gupta 1 , Jing Gao 2 , Yizhou Sun 1 , Jiawei Han 1 1 UIUC 2 SUNY , Buffalo

Integrating Community Matching and Outlier Detection for Mining Evolutionary Community Outliers. Manish Gupta 1 , Jing Gao 2 , Yizhou Sun 1 , Jiawei Han 1 1 UIUC 2 SUNY , Buffalo KDD 2012 Beijing, China. Temporal Datasets are Ubiquitous. Time Series. Evolving networks.

540 views • 41 slides

Analysis with Extremely Large Datasets

434 views • 31 slides

SDAP: A Secure Hop-by-Hop Data Aggregation Protocol for Sensor Networks

SDAP: A Secure Hop-by-Hop Data Aggregation Protocol for Sensor Networks. Yi Yang, Xinran Wang, Sencun Zhu and Guohong Cao April 24, 2007 Presented by Nicky Mahilani CSC 774 In-class presentation. Acknowledgement: Based on slides provided by Author. Outline.

645 views • 30 slides

On Community Outliers and their Efficient Detection in Information Networks

On Community Outliers and their Efficient Detection in Information Networks. Jing Gao , Feng Liang, Wei Fan, Chi Wang, Yizhou Sun, and Jiawei Han SIGKDD, 2010 Presented by Hung-Yi Cai 2011/2/10. Outlines. Motivation Objectives Related Work Methodology Experiments Conclusions

429 views • 27 slides

Outlier removal

617 views • 26 slides

Compensation for Measurement Errors Due to Mechanical Misalignments in PCB Testing

Compensation for Measurement Errors Due to Mechanical Misalignments in PCB Testing. Anura P. Jayasumana , Yashwant K. Malaiya , Xin He, Colorado State University Kenneth P. Parker and Stephen Hird Agilent Technologies.

384 views • 24 slides

Context

An Algorithmic and Systematic Approach for Improving Robustness of TOA-based Localization Yongcai Wang , Lei Song Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, Beijing, China in EUC2013, Nov.13, 2013. Context.

330 views • 22 slides

Ch. Eick : Support Vector Machines: The Main Ideas

359 views • 19 slides

An Unbiased Distance-based Outlier Detection Approach for High Dimensional Data

An Unbiased Distance-based Outlier Detection Approach for High Dimensional Data. DASFAA 2011 By Hoang Vu Nguyen, Vivekanand Gopalkrishnan and Ira Assent Presented By Salman Ahmed Shaikh (D1). Contents. Introduction Subspace Outlier Detection Challenges Objectives of Research

294 views • 18 slides

Intrusion Detection Using Data Mining

Intrusion Detection Using Data Mining. By Anshu Veda(04329022) Prajakata Kalekar(04329008) Anirudha Bodhankar(04329003) Under the Guidance of Prof Sunita Sarawagi. Problem Definition.

406 views • 17 slides

Modeling Prosodic Sequences with K-Means and Dirichlet Process GMMs

404 views • 17 slides

D.Y. Ye and Z.J. Chen College of Math. & Computer Science Fuzhou University, China

A New Algorithm for High Dimensional Outlier Detection Based on Constrained Particle Swarm Intelligence. D.Y. Ye and Z.J. Chen College of Math. & Computer Science Fuzhou University, China. Inadequateness of proximity-based notion of outliers in high dimensional space.

265 views • 15 slides

Level 2 Ocean Salinity

Level 2 Ocean Salinity. 16-17 May 2013. A3TEC. TEC from Stokes 3. Apply OTT, perform outlier detection, un-apply OTT: only use good TB Select A3 measurements with high incidence angles close to the track eg -0.025 < xi < 0.025 and 0.15 < eta < 0.2

267 views • 12 slides

An extended K-means++ with mixed attributes for outlier detection

- Progress presentation -. An extended K-means++ with mixed attributes for outlier detection. Presented by Miss Sarunya Kanjanawattana. Examination Committee. Dr. Sumanta Guha (Chairperson) Prof. Dr. Phan Minh Dung (Committee) Dr. Matthew N. Dailey (Committee). Agenda. Contribution

217 views • 11 slides

Yahoo! DAIS Research Excellence Award Competition Manish Gupta

287 views • 6 slides

GUHA method in Data Mining Esko Turunen Tampere University of Technology Tampere, Finland

GUHA method in Data Mining Esko Turunen Tampere University of Technology Tampere, Finland. Data Mining in a Nutshell

162 views • 3 slides