PPT-Graph Data Mining with Map-Reduce

Nima Sarshar PhD INTUIT Inc Nimasarsharintuitcom Intuit Graphs and Me Me Largescale graph data processing complex networks analysis graph algorithms Intuit QuickBooks TurboTax . Bo Zong. 1. w. ith . Yinghui . Wu. 1. , . Jie . Song. 2. , . Ambuj K. . Singh. 1. , . Hasan . Cam. 3. , . Jiawei . Han. 4. , . and Xifeng . Yan. 1. 1. UCSB, . 2. LogicMonitor, . 3. Army . Research Lab, . : Distributed Co-clustering with Map-Reduce. S. Papadimitriou, J. Sun. IBM T.J. Watson Research Center. Speaker:. 0356169. 吳宏君. 0350741. . 陳威遠. 0356042 . 洪浩哲. Outline. Introduction. Prajwal Shrestha. Department of Computer Science. The . University . of Vermont. Spring 201. 5. Original Authors. This presentation is based on the paper. Zaki. MJ (2002). Efficiently mining frequent trees in a forest. . Chapter 1. Kirk Scott. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Graphs are a flexible & unifying model. Scalable similarity searches through novel index structure. Mining of significant fragments in collections. Classification of compounds based on significant fragments . dimensional data . sets into . simplicial complexes . with far . fewer points . which can . capture topological . and geometric information at . a specified resolution. .. v. 2. e. 2. e. 1. e. 3. v. 1. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Chapter 7 : Advanced Frequent Pattern Mining. Jiawei Han, Computer Science, Univ. Illinois at Urbana-Champaign. , 2017. 1. October 28, 2017. Data Mining: Concepts and Techniques. 2. Chapter 7 : Advanced Frequent Pattern Mining. Outline. Link Analysis Concepts. Metrics for Analyzing Networks. PageRank. HITS. Link Prediction. 2. Link Analysis Concepts. Link. A relationship between two entities. Network or Graph. A collection of entities and links between them. Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. and Observations. Presenter: . Vivi. Ma. A . Peta. -Scale Graph Mining System . CONTENT. Background. PEGUSUS. PageRank Example. Performance . and Scalability. Real . World . Applications. BACKGROUND. Mail : raphaeljuwe@gmail.com Mobile : K2S4 8 PS7647272 GitHub: github.comOraphaeljuwe LinkedIn : ng. linkedin.comOinOraphaeljuwe Research Interests Sequential Pattern Mining and Knowledge Discovery Chong Ho (Alex) Yu. Agenda. What is data visualization?. What isn’t data visualization?. What is the difference between dynamic, semi-dynamic, and non-dynamic (static) visualization systems?. Why do we need data visualization?. Bamshad Mobasher. DePaul University. 2. From Data to Wisdom. Data. The raw material of information. Information. Data organized and presented by someone. Knowledge. Information read, heard or seen and understood and integrated.