CS 63015 Data Mining Techniques Kent State

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  • Kent State CS 63015 Fall 2008
    A Framework For Community Identication in Dynamic Social Networks Chayant Tantipathananandh Dept. of Computer Science University of Illinois at Chicago Chicago, IL 60304 Tanya Berger-Wolf Dept. of Computer Science University of Illinois at Chicago C
  • Kent State CS 63015 Fall 2008
    Page Rank PageRank Intuition: solve the recursive equation: a page is important if important pages link to it. Maximailly: importance = the principal eigenvector of the stochastic matrix of the Web. A few fixups needed. Stochastic Matrix of the
  • Kent State CS 63015 Fall 2008
    Modularity and community structure in networks MEJ Newman University of Michigan -Harsh Joshi Pervious Work Graph Partitioning - Minimum Cuts - Spectral Partitioning Applications: - Parallel computing - VLSI design and other CAD applications Perv
  • Kent State CS 63015 Fall 2008
    Summarization of Frequent Pattern Mining What is FPM? Why being frequent is so important? Application of FPM Decision make/Business Software Debugging Bioinformatics Indexing Clustering/Classification/Association Rule Other data mining ta
  • Kent State CS 63015 Fall 2008
    SCAN: A Structural Clustering Algorithm for Networks Xiaowei Xu Zhidan Feng Nurcan Yuruk Thomas Schweiger Presented By Maen Hammad Features Identify Clusters Fast algorithm O(m) Identify hubs and outliers nodes Example HUB OUTLIER Related Wor
  • Kent State CS 63015 Fall 2008
    Classification: Basic Concepts and Decision Trees A programming task Classification: Definition Given a collection of records (training set ) Each record contains a set of attributes, one of the attributes is the class. Find a model for class
  • Kent State CS 63015 Fall 2008
    Cascading Behavior in Networks: Algorithmic and Economic Issues Jon Kleinberg Abstract The ow of information or inuence through a large social network can be thought of as unfolding with the dynamics of an epidemic: as individuals become aware of
  • Kent State CS 63015 Fall 2008
    A measure of betweenness centrality based on random walks Author: M. E. J. Newman Presented by: Amruta Hingane Department of Computer Science Kent State University Overview Introduction Centrality Measures Types Of Betweenness Random-walk Betw
  • Kent State CS 63015 Fall 2008
    Substructure Similarity Search in Graph Databases Xifeng Yan Philip S. Yu Jiawei Han University of Illinois at Urbana-Champaign, {xyan, hanj}@cs.uiuc.edu IBM T. J. Watson Research Center, psyu@us.ibm.com ABSTRACT Advanced database systems face
 
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