PPT-Spectral clustering of graphs

Sushmita Roy sroybiostatwiscedu Computational Network Biology Biostatistics amp Medical Informatics 826 Computer Sciences 838 httpscompnetbiocoursediscoverywiscedu Nov 3 rd Nov 10. The First Step in Quantitative Spectral Analysis. Richard Gray. Appalachian State University. MK Spectral Classification: 1943 – 2013. 70 years of contributions to stellar astronomy. Discovery of the spiral structure of the Galaxy (Morgan, . and Physical Interaction . Datasets. Manikandan Narayanan, Adrian Vetta, Eric E. Schadt, Jun Zhu. PLoS Computational Biology 2010. Presented by: Tal Saiag. Seminar in Algorithmic Challenges in Analyzing Big Data* in Biology and . April 22, 2010. Last Time. GMM Model Adaptation. MAP (Maximum A Posteriori). MLLR (Maximum Likelihood Linear Regression). UMB-. MAP. for speaker recognition. Today. Graph Based Clustering. Minimum Cut. E. Tognoli, . october. 9. th. , 2008, HBBL meeting. Peaks~floor. floor. peak. Interim question 1: why are there more peaks . in structured behavioral tasks? . Steady-State paradigms and structured behavioral tasks. Daniel A. Spielman. Yale University. AMS Josiah Willard Gibbs Lecture. January . 6. , 2016 . From Applied to Pure Mathematics. Algebraic and Spectral Graph Theory. . . Sparsification. :. a. pproximating graphs by graphs with fewer edges. Frank Lin. 10-710 Structured Prediction. School of Computer Science. Carnegie Mellon . University. 2011-11-28. Talk Outline. Clustering. Spectral Clustering. Power Iteration Clustering (PIC). PIC with Path Folding. Vaibhav. . Mallya. EECS 767. D. . Radev. 1. Agenda. Agenda. Basic Definitions. Girvan-Newman Algorithm. Donetti. -Munoz Spectral Method. Karypis. -Kumar Multi-level Partitioning. Graclus. GraphClust. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Nov 3. rd. 2016. RECAP. Daniel A. Spielman. Yale University. AMS Josiah Willard Gibbs Lecture. January . 6. , 2016 . From Applied to Pure Mathematics. Algebraic and Spectral Graph Theory. . . Sparsification. :. a. pproximating graphs by graphs with fewer edges. The vertical scale is too big or too small, or skips numbers, or doesn’t start at zero.. The graph isn’t labeled properly.. Data is left out.. But some real life misleading graphs go above and beyond the classic types. Some are intended to mislead, others are intended to shock. And in some cases, well-meaning individuals just got it all plain wrong. These are some of my favorite recent-history misleading graphs from real life.. What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. Unsupervised . learning. Seeks to organize data . into . “reasonable” . groups. Often based . on some similarity (or distance) measure defined over data . elements. Quantitative characterization may include. 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering.