PPT-Clustering Patrice Koehl

Department of Biological Sciences National University of Singapore http wwwcsucdavisedu koehl TeachingBL5229 koehlcsucdavisedu Clustering is a hard problem Many possibilities What is best clustering . . Coordinator, California Summer Meal Coalition. Strategies for Schools:. Building a Robust Summer Food Program in Tough Economic Times. Welcome! . Patrice Chamberlain. , California Summer Meal Coalition. Ensemble Clustering. unlabeled . data. ……. F. inal . partition. clustering algorithm 1. combine. clustering algorithm . N. ……. clustering algorithm 2. Combine multiple partitions of . given. data . Machine . Learning . 10-601. , Fall . 2014. Bhavana. . Dalvi. Mishra. PhD student LTI, CMU. Slides are based . on materials . from . Prof. . Eric Xing, Prof. . . William Cohen and Prof. Andrew Ng. 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. Andrew Koehl Roberts Wesleyan College Biography Andrew Koehl is Professor of Philosophy at Roberts Wesleyan College. He received his Ph.D. in philosophy from the University of Notre Dame, with specia 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. TE-MPE Technical Meeting : 22/03/2012 . Topics. Patrice Nouvel – March 2012. 2. CLIC Machine Protection . Patrice Nouvel – March 2012. -. 3. -. - Failures classification and their related protection system -. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Nov 3. rd. , Nov 10. 1960-1961. Belgian Control of the Congo: 1884-1960 . Decolonization in Africa. Pressure for Independence. Patrice Lumumba at the round table in Belgium, 1960. An Independent Congolese Government. Patrice Lumumba. Department of Computer Science. Genome Center. UC Davis. The three I’s of Big Data. Big Data is:. Ill-defined . (what is it?). Immediate. (we need to do something about it now). Intimidating. (what if we don’t). 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. 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 .