'Graph mining' diaporamas de présentation

15-826: Multimedia Databases and Data Mining

15-826: Multimedia Databases and Data Mining. Lecture #26: Graph mining - patterns Christos Faloutsos. Must-read Material. Michalis Faloutsos, Petros Faloutsos and Christos Faloutsos, On Power-Law Relationships of the Internet Topology, SIGCOMM 1999.

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15-826: Multimedia Databases and Data Mining

15-826: Multimedia Databases and Data Mining. Lecture #26: Graph mining - patterns Christos Faloutsos. Must-read Material. [Graph minining textbook] Deepayan Chakrabarti and Christos Faloutsos Graph Mining: Laws, Tools and Case Studies , Morgan Claypool, 2012 Part I (patterns).

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Large Graph Mining

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CSCE822 Data Mining and Warehousing

CSCE822 Data Mining and Warehousing. Lecture 16 Graph Mining MW 4:00PM-5:15PM Dr. Jianjun Hu http://mleg.cse.sc.edu/edu/csce822. University of South Carolina Department of Computer Science and Engineering. Roadmap. What, Why Graph Mining? Methods for Mining Frequent Subgraphs

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Graph Mining Applications in Machine Learning Problems

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Trust, Influence and Bias in Social Media

Trust, Influence and Bias in Social Media. Anupam Joshi Joint work with Tim Finin and several students Ebiquity Group, UMBC joshi@cs.umbc.edu http://ebiquity.umbc.edu/. Knowing & Influencing your Audience. Your goal is to campaign for a presidential candidate

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Seminar in Bioinformatics

Seminar in Bioinformatics. An efficient algorithm for detecting frequent subgraphs in biological networks. Paper by: M. Koyuturk, A. Grama and W. Szpankowski Appeared in: Bioinformatics, Vol. 20, Sup. 1, 2004, pages i200-i207. Presented by: Royi Ronen. Abstract. Motivation

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Ex-MATE: Data-Intensive Computing with Large Reduction Objects and Its Application to Graph Mining

Ex-MATE: Data-Intensive Computing with Large Reduction Objects and Its Application to Graph Mining. Wei Jiang and Gagan Agrawal. Outline. Background System Design of Ex-MATE Parallel Graph Mining with Ex-MATE Experiments Related Work Conclusion. Outline. Background

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??????????????????????? Efficient Maximal Pattern Discovery from Massive Geometric Graphs

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Graph Data Management

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Graph Data Management

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Special Topics in Database Systems

Special Topics in Database Systems. Martin Ester Simon Fraser University School of Computing Science CMPT 884 Spring 2009. Introduction. [Fayyad, Piatetsky-Shapiro & Smyth 96].

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Direct Mining of Discriminative and Essential Frequent Patterns via Model-based Search Tree

Direct Mining of Discriminative and Essential Frequent Patterns via Model-based Search Tree. How to find good features from semi-structured raw data for classification. Wei Fan, Kun Zhang, Hong Cheng, Jing Gao, Xifeng Yan, Jiawei Han, Philip S. Yu, Olivier Verscheure. Feature Construction.

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Use of Ranked Cross Document Evidence Trails for Hypothesis Generation

Use of Ranked Cross Document Evidence Trails for Hypothesis Generation. Presenter : Jiang-Shan Wang Authors : Rohini K. Srihari, Li Xu, Tushar Saxena. ???????? National Yunlin University of Science and Technology. SIGKDD 2008. Outline. Motivation Objective Methodology Experiments

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COMP790-90 Research Seminar BCB 713 Module Data Mining Concepts, Algorithms, and Applications

COMP790-90 Research Seminar BCB 713 Module Data Mining Concepts, Algorithms, and Applications. Instructor: Wei Wang Spring 2011. Welcome!. Instructor: Wei Wang Homepage: www.cs.unc.edu/~weiwang Office: 316 Sitterson Hall Email: weiwang@cs.unc.edu Office hour: by appointment. Overview.

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AMCS/CS 340: Data Mining

Top10 DM Algorithms. AMCS/CS 340: Data Mining. Xiangliang Zhang King Abdullah University of Science and Technology. Story behind the Top10. What are the most influential data mining algorithms in the research community ?

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Christoph F. Eick

COSC 6335 Fall 2013 Project3: Something Interesting About Finding Interesting Associations in Large Amounts of Data. Christoph F. Eick. Project3 (Group Project Association Analysis). 8 Groups of 4: Choose your own theme: Implement an association analysis algorithm

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