PPT-Mixtures, clustering, spatial

amp dynamic point processes and big data sets Mike West Department of Statistical Science Duke University cellular phenotypes in vaccine adjuvant studies Immune response studies. However the application to large spatial databases rises the following requirements for clustering algorithms minimal requirements of domain knowledge to determine the input parameters discovery of clusters with arbitrary shape and good ef64257cienc Assume you have to do feature selection for a classification task. . What . are the characteristics of features (attributes) you might remove from the dataset prior to learning the classification algorithm. what is . homegeneous. ?. What is solute ?. Uniformly mixed – only see one layer. substance that dissolve in a solvent. What is solvent?. Is a substance that dissolves the solute. What is solution ?. Mixtures may be separated by many different techniques based on differing physical and/or chemical properties. Sorting. Simply picking apart the different components. This can be easy and obvious…. 11, 2010. Discussion of Midterm Exam. Assume an association . rule if smoke then cancer . has a confidence of 86% and a high lift of 5.4. What does this tell you about the relationship of smoking and cancer? . 12, 2010. How does post decision tree post-pruning work? What is the purpose of applying post-pruning in decision tree learning? . What are the characteristics of representative-based/ prototype-based clustering algorithms—what do they all have in common? . Asymptotics. Yining Wang. , Jun . zhu. Carnegie Mellon University. Tsinghua University. 1. Subspace Clustering. 2. Subspace Clustering Applications. Motion Trajectories tracking. 1. 1 . (. Elhamifar. Eric . Feigelson. Penn State University. Arcetri. Observatory, April 2014. Background on Spatial Point Processes. Study of spatial point processes is a part of the field of spatial statistics that includes: graph, map, network data; lattice data (e.g. images); . David C Maré . Motu. Economic and Public Policy Research . & University of Waikato. with . Ruth Pinkerton and Jacques Poot. New Zealand Treasury National Institute of Demographic. David . Harel. and . Yehuda. . Koren. KDD 2001. Introduction. Advances in database technologies resulted in huge amounts of spatial data. The characteristics of spatial data pose several difficulties for clustering algorithms.. Chapter 5. Ex:. Melting. Freezing. Boiling/evaporation. Condensation. Sublimation. Dissolving. Bending. Crushing. Breaking. Chopping. Filtration. distillation. Physical change. Does not make a different substance. Large Graphs. David . Hallac. , Jure . Leskovec. , Stephen . Boyd . Stanford . University. Presented by Yu Zhao. What is this paper about. Lasso problem. The lasso solution is unique when rank(X) = p, because the criterion is strictly convex.. [ & dynamic ] . point processes and big data sets . Mike . West. Department of Statistical Science. Duke University. . cellular phenotypes in vaccine adjuvant studies . Immune response studies. 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.