PPT-Mining Billion-Node

Graphs Patterns and Algorithms Christos Faloutsos CMU Thank you Dr ChingHao Eric Mao Prof Kenneth Pao Taiwan Aug12 C Faloutsos CMU 2 C Faloutsos CMU 3 Our goal Open source system for mining huge graphs. LECTURE . 13. Absorbing Random walks. Coverage. ABSORBING RANDOM WALKS. Random walk with absorbing nodes. What happens if we do a random walk on this graph? What is the stationary distribution?. All the probability mass on the red . Mining Frequent Patterns. Ali Javed. CS:332, April 20. th. , 2015. Slides by . Afsoon. . Yousefi. Jiawei. Han, . Jian. Pei and . Yiwen. Yin. . School of Computer Science. Simon Fraser University. Nima Sarshar, Ph.D.. INTUIT . Inc. ,. Nima_sarshar@intuit.com . Intuit, . Graphs and Me. Me: . Large-scale graph data processing, complex networks analysis, graph algorithms … . Intuit: . QuickBooks, TurboTax, . 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. . Sam Chikowore. – Exporien Mining. Zimbabwe Mining and Infrastructure Indaba . 2013.. WHO ARE THEY?. THE ARTISANAL MINERS. THE MINING CO-OPERATIVES. WOMEN MINING CO-OPERATIVES. THE JUNIOR MINING COMPANIES. Mining Frequent Patterns. Afsoon. . Yousefi. CS:332, March 24. th. , 2014. Inspired by Song Wang slides. Jiawei. Han, . Jian. Pei and . Yiwen. Yin. . School of Computer Science. Simon Fraser University. using Boa. Robert Dyer. These . research activities . supported . in part by the US National Science . Foundation (. NSF) . grants. CNS. -15-13263, CNS-15-12947, CCF. -15-18897, CCF-15-18776, CCF-14-23370, CCF. M. ining . Techniques on Survey . D. ata . using R and . Weka. Supunmali Ahangama. 29/11/2013. Outline. Introduction to data mining in R . Introduction to data mining in . Weka. Example. R. X. 2. What is R?. Barter. Goods *BECOME* Money. Acceptable. Durable. Portable. Scarce. Divisible. Recognizable. Gold Storage -> Paper Receipts. England, 17. th. Century. Safety for travelers. Charles I’s Royal Mint loan (1640). Usage. at a Very Large . Scale. Robert Dyer. These . research activities . supported . in part by the US National Science . Foundation (. NSF) . grants. CNS. -15-13263, CNS-15-12947, CCF. -15-18897, CCF-15-18776, CCF-14-23370, CCF. 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. Decision Trees on MapReduce CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu Decision Tree Learning Give one attribute (e.g., lifespan), try to predict the value of new people’s lifespans by means of some of the other available attribute Dr. Sampath Jayarathna. Old Dominion University. . CS 495/595. Introduction to Data Mining. 1. Credit for some of the slides in this lecture goes to . Xun. Luo and Shun Liang. Introduction. Apriori. Prepared by David Douglas, University of Arkansas. Hosted by the University of Arkansas. 1. IBM . Clustering. Hosted by the University of Arkansas. 2. Quick Refresher. . DM used to find previously unknown meaningful patterns in data.