dimension values of a record are mapped to m pixels at the corresponding

# Dimension values of a record are mapped to m pixels

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dimension values of a record are mapped to m pixels at the corresponding positions in the windows The colors of the pixels reflect the corresponding values 28 (a)Income (b) Credit Limit (c) Transaction Volume (d) Age

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Data Mining Exploratory Data Analysis Laying Out Pixels in Circle Segments 29 To save space and show the connections among multiple dimensions, space filling is often done in a circle segment Laying out pixels in circle segment Representing a data record in circle segments Representing about 265,000 50-dimensional Data Items with the ‘Circle Segments’ Technique
Data Mining Exploratory Data Analysis Geometric Projection Visualization Techniques 30 Visualization of geometric transformations and projections of the data Methods Direct visualization Scatterplot and scatterplot matrices Landscapes Projection pursuit technique: Help users find meaningful projections of multidimensional data Prosection views Hyperslice Parallel coordinates

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Data Mining Exploratory Data Analysis Direct Data Visualization 31 R i b b o n s w i t h T w i s t s B a s e d o n V o r t i c i t y
Data Mining Exploratory Data Analysis Scatterplot Matrices 32 Matrix of scatterplots (x- y-diagrams) of the k-dim. data [total of () scatterplots] Used by ermission of M. Ward, Worcester Polytechnic Institute

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Data Mining Exploratory Data Analysis Landscapes 33 Visualization of the data as perspective landscape The data needs to be transformed into a (possibly artificial) 2D spatial representation which preserves the characteristics of the data news articles visualized as a landscape Used by permission of B. Wright, Visible Decisions Inc.
Data Mining Exploratory Data Analysis Parallel Coordinates equidistant axes which are parallel to one of the screen axes and correspond to the attributes The axes are scaled to the [minimum, maximum]: range of the corresponding attribute Every data item corresponds to a polygonal line which intersects each of the axes at the point which corresponds to the value for the attribute 34

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Data Mining Exploratory Data Analysis Icon-Based Visualization Techniques 35 Visualization of the data values as features of icons Typical visualization methods Chernoff Faces Stick Figures General techniques Shape coding: Use shape to represent certain information encoding Color icons: Use color icons to encode more information Tile bars: Use small icons to represent the relevant feature vectors in document retrieval
Data Mining Exploratory Data Analysis Chernoff Faces A way to display variables on a two-dimensional surface, e.g., let x be eyebrow slant, y be eye size, z be nose length, etc. The figure shows faces produced using 10 characteristics--head eccentricity, eye size, eye spacing, eye eccentricity, pupil size, eyebrow slant, nose size, mouth shape, mouth size, and mouth opening): Each assigned one of 10 possible values, generated using Mathematica (S. Dickson) 36 REFERENCE: Gonick, L. and Smith, W.

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