PPT-Cluster Analysis

Kmeans Clustering Each cluster is represented by the center of the cluster The algorithm steps Step 1 Choose the number of clusters k Step 2 Randomly generate k clusters and determine the cluster centers or directly generate . Grouping Cases or Variables. Clustering Cases. Goal is to cluster cases into groups based on shared characteristics.. Start out with each case being a one-case cluster.. The clusters are located in k-dimensional space, where . Methods of cluster analysis. Goals 1. We want to identify groups of similar artifacts or features or sites or graves, etc that represent cultural, functional, or chronological differences. We want to create groups as a measurement technique to see how they vary with external variables. Basics of clustering. Data . structuring tool . generally used as exploratory . rather than confirmatory tool. . Organizes data . into meaningful taxonomies in which groups . are relatively . homogeneous with respect to a specified set . Lauren Young. Office of Institutional Analysis. University at Buffalo. 1. Why Finish in 4? The Institutional View. 4-year graduation increasingly held up as an indicator of institutional quality and effectiveness. Structures in . Open Source Software Development . Digital Traces & Qualitative . Inquiry. Aron Lindberg. Case Western Reserve University. Research Problems. How do OSS projects match the complexity of problems with requisite complexity of routines?. Sarah-Jane Richards. Supervised by Dr. Rebecca . Notman. What is the Scientific importance of the project? . Cryopreservation of biological materials. .. Blood cell, stem cells and even whole organs. Finding arrays (dimensions) and chunks. Multidimensional scaling. MDS is . a multivariate . data-reduction technique. . Like factor analysis, it is used to tease out underlying relations among a set of observations. . Oliver van . Kaick. 1,4 . . Kai . Xu. 2. . Hao. Zhang. 1. . Yanzhen. Wang. 2. . Shuyang. Sun. 1. Ariel Shamir. 3. Daniel Cohen-Or. 4. 4. Tel Aviv University. 1. Simon . Fraser University. . . Chong Ho Yu. Why do we look at . grouping (cluster) patterns?. This regression model yields 21% variance explained.. The . p. value is not significant (p=0.0598). But remember we must look at (visualize) the data pattern rather than reporting the numbers. Chong Ho Yu. Crime hot spots. How can criminologists find the hot spots?. Data reduction. Group variables into factors or components based on people’s response patterns. PCA. Factor analysis. Group people into groups or clusters based on variable patterns. Why do we analyze livelihoods?. Food security . analysis aims at informing geographical . and. socio-economic targeting. Livelihood analysis allows us to answer . one of the key basic questions of food security analysis: “who are the food insecure. Unsupervised Learning DSCI 415 Brant Deppa, Ph.D. Professor of Statistics & Data Science Winona State University bdeppa@winona.edu The Entire Course in One Day !?!? Course Topics Introduction to Unsupervised Learning S. ystems . S. ophistication. : An . Empirical . T. axonomy . of European . Acute Care Hospitals . Placide . POBA-NZAOU. University of Quebec in Montreal, Canada. Sylvestre. . UWIZEYEMUNGU . University of Quebec in . Dr.Chayada. Bhadrakom. Agricultural and Resource Economics, . Kasetsart. University. Cluster analysis . Lecture / Tutorial outline. Cluster analysis. Example of cluster analysis. Work on SPSS. Introduction.