The Cartoon Guide to Statistics New York Harper Perennial p 212 1993 Weisstein

The cartoon guide to statistics new york harper

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The Cartoon Guide to Statistics. New York: Harper Perennial, p. 212, 1993 Weisstein, Eric W. "Chernoff Face." From MathWorld --A Wolfram Web Resource. mathworld.wolfram.com/ChernoffFace.html
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Data Mining Exploratory Data Analysis Stick Figure A census data figure showing age, income, gender, education, etc. A 5-piece stick figure (1 body and 4 limbs w. different angle/length) 37
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Data Mining Exploratory Data Analysis Hierarchical Visualization Techniques 38 Visualization of the data using a hierarchical partitioning into subspaces Methods Dimensional Stacking Worlds-within-Worlds Tree-Map Cone Trees InfoCube
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Data Mining Exploratory Data Analysis Visualizing Complex Data and Relations: Tag Cloud & Tree-Map Tag cloud visualizing user-generated tags. The importance of tag is represented by font size/color Popularly used to visualize word/phrase distributions 39 KDD 2013 Research Paper Title Tag Cloud Newsmap: Google News Stories in 2005 Tree-Map display hierarchical data as a set of nested rectangles.
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Data Mining Exploratory Data Analysis Visualizing Complex Data and Relations: Social Networks Visualizing non-numerical data: social and information networks 40 A typical network structure A social network organizing information networks
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Data Mining Exploratory Data Analysis Measuring Data Similarity and Dissimilarity Data Matrix and Dissimilarity Matrix Proximity Measures for Nominal Attributes Proximity Measures for Binary Attributes Dissimilarity of Numeric Data: Minkowski Distance Proximity Measures for Ordinal Attributes Dissimilarity for Attributes of Mixed Types Cosine Similarity 41
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Data Mining Exploratory Data Analysis Measuring Data Similarity and Dissimilarity 42 A cluster is a collection of data objects such that the objects within a cluster are similar to one another and dissimilar to the objects in other clusters. Outlier analysis also employs clustering-based techniques to identify potential outliers as objects that are highly dissimilar to others.
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Data Mining Exploratory Data Analysis Similarity, Dissimilarity, and Proximity 43 Similarity measure or similarity function A real-valued function that quantifies the similarity between two objects Measure how two data objects are alike: The higher value, the more alike Often falls in the range [0,1]: 0: no similarity; 1: completely similar Dissimilarity (or distance ) measure Numerical measure of how different two data objects are In some sense, the inverse of similarity: The lower, the more alike Minimum dissimilarity is often 0 (i.e., completely similar) Range [0, 1] or [0, ∞) , depending on the definition Proximity usually refers to either similarity or dissimilarity
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Data Mining Exploratory Data Analysis Data Matrix and Dissimilarity Matrix 44 Data matrix A data matrix of data points with dimensions Dissimilarity (distance) matrix data points, but registers only the distance d ( i, j ) (typically metric)
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