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
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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
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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.
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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
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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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