PDF-A Modelbased Approach to Attributed Graph Clustering Z

ntuedusg Yiping Ke Institute of High Performance Computing Singapore keypihpcastaredusg Yi Wang Department of Computer Science National University of Singapore wangycompnusedusg Hong Cheng Department of Systems Engineering and Engineering Management. Why graph clustering is useful?. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. Why graph clustering is useful?. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. 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. Attributed . Graphs . Yu Su. University of California at Santa Barbara. with . Fangqiu. Han, Richard E. . Harang. , and . Xifeng. Yan . Introduction. A Fast Kernel for Attributed Graphs. Graph Kernel. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Nov 1. st. 2016. Some material is adapted from lectures from Introduction to Bioinformatics. Cluster Analysis. Outline. Introduction to Cluster Analysis. Types of Graph Cluster Analysis. Algorithms for Graph Clustering. k-Spanning Tree. Shared Nearest Neighbor. Betweenness Centrality Based. Highly Connected Components. 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. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. Outline. Min s-t cut problem. 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. 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. kindly visit us at www.nexancourse.com. Prepare your certification exams with real time Certification Questions & Answers verified by experienced professionals! We make your certification journey easier as we provide you learning materials to help you to pass your exams from the first try. Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. Tricky because .