PPT-Clustering Heterogeneous Samples During

Model Selection Kathleen Gates PhD Assistant Professor LL Thurstone Psychometric Lab Department of Psychology Research Group Stephanie Lane MA Teague Henry BS Zachary Fisher MS. edu yintaouiucedu hanjcsuiucedu ABSTRACT A heterogeneous information network is an information network composed of multiple types of objects Cluster ing on such a network may lead to better understanding of both hidden structures of the network and t algorithm and its application in single . cell . RNA-. seq. data analysis and identification of gain or loss of functions of somatic mutations . Chi Zhang, Ph.D.. Center for Computational Biology and Bioinformatics. 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 . Rare . Event. Analysis with Multiple Failure Region Coverage. Wei Wu. 1. , Srinivas Bodapati. 2. , Lei He. 1,3. 1 Electrical Engineering Department, UCLA. 2 Intel Corporation. 3 . State . Key Laboratory of ASIC and Systems, . cyanobacterium. . Prochlorococcus. Alyssa Kent. 6/1/2013. C-MORE Student Symposium. P. hylogeography. Emerson K J et al. PNAS 2010;107:16196-16200. Relationships between gene . genealogies--. phylogenetics. 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 . 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. C. ontrol of . H. eterogeneous . L. arge-Scale Systems of . A. utonomous . V. ehicles (. TECHLAV. ). TECHLAV Annual Meeting. Greensboro, NC. May 31-June 1, 2017. http://techlav.ncat.edu. /. Task Allocation Using Parallelized Clustering and Auctioning Algorithms for Heterogeneous Robotic Swarms Operating on a Cloud Network. Objective: develop technologies to improve computer performance. . . 1. Processor. Generation. Max. Clock. Speed (GHz). Max. Numberof Cores. Max. RAM. Bandwidth (GB/s). Max. Peak Floating Point (Gflop/s). 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 . Stephen J. . Hardiman. *. Capital Fund Management . France. Liran. . Katzir. Advanced Technology Labs. Microsoft Research, Israel. *Research was conducted while the author was . unaffiliated. Motivation: Social Networks. Collections through . Contextual Focal Points. Kai . Xu. , . Rui. Ma, . Hao. Zhang, . Chenyang. Zhu,. Ariel . Shamir,. . Daniel Cohen-Or,. . . Hui. . Huang. Shenzhen . VisuCA. Key Lab / . 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 .