PPT-Mr. Scan: Efficient Clustering with

MRNet and GPUs Evan Samanas and Ben Welton Densitybased clustering Discovers the number of clusters Finds oddlyshaped clusters 2 Mr Scan Efficient Clustering with MRNet and GPUs. second view is used to relate CW to another graph clustering algorithm, namely MCL (van Dongen, 2000). We use the following notation throughout this paper: Let G=(V,E) be a weighted graph with nodes ( . aLGORITHMS. Ryan Tinsley. Brandon Lile. May 9th, 2014. Bioinformatics . What?. Why?. Remains large frontier. Goals:. Organize and serve data. Develop tools to analyze. Interpret results . 2. Clustering. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . Viola-Jones Classifier . based Face Detection Algorithm. Sharmila Shridhar, Vinay Gangadhar, . Ram Sai Manoj. ECE . 759 Project Presentation. Fall . 2015. University of Wisconsin - Madison. 1. Executive Summary. . JOHN P. PERDEW, TEMPLE UNIVERSITY. . JIANWEI SUN, ADRIENN RUZSINSZKY, AND JOHN P. PERDEW, PHYS. REV. LETT. 115, 036402 (2015). SUN, REMSING, ZHANG, SUN, RUZSINSZKY, PENG, YANG, PAUL, WAGHMARE, WU, KLEIN, AND PERDEW, NATURE CHEM., TO . issue in . computing a representative simplicial complex. . Mapper does . not place any conditions on the clustering . algorithm. Thus . any domain-specific clustering algorithm can . be used.. We . 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. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . 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. Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values.. 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 .