CSCI 347 / CS 4206: Data Mining Module 05: WEKA Topic 04: Data Preparation Tools.

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Presentation transcript:

CSCI 347 / CS 4206: Data Mining Module 05: WEKA Topic 04: Data Preparation Tools

Explorer: Pre-Processing the Data  Data can be imported from a file in various formats: ARFF, CSV, C4.5, binary  Data can also be read from a URL or from an SQL database (using JDBC)  Pre-processing tools in WEKA are called “filters”  WEKA contains filters for:  Discretization, normalization, resampling, attribute selection, transforming and combining attributes, … 2

3 WEKA GUI Chooser

4 Preprocessing Data in WEKA

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10 Preprocessing Data in WEKA

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17 Preprocessing Data in WEKA

18 Preprocessing Data in WEKA

19 Preprocessing Data in WEKA

20 Preprocessing Data in WEKA

21 Preprocessing Data in WEKA

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23 Preprocessing Data in WEKA

WEKA Explorer: Attribute Selection  Panel that can be used to investigate which (subsets of) attributes are the most predictive ones  Attribute selection methods contain two parts:  A search method: best-first, forward selection, random, exhaustive, genetic algorithm, ranking  An evaluation method: correlation-based, wrapper, information gain, chi-squared, …  Very flexible: WEKA allows (almost) arbitrary combinations of these two 24

25 Attribute Selection in WEKA

26 Attribute Selection in WEKA

27 Attribute Selection in WEKA

28 Attribute Selection in WEKA

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30 Attribute Selection in WEKA

31 Attribute Selection in WEKA

32 Attribute Selection in WEKA

33 Attribute Selection in WEKA

Explorer: Data Visualization  Visualization very useful in practice: e.g. helps to determine difficulty of the learning problem  WEKA can visualize single attributes (1-d) and pairs of attributes (2-d)  Color-coded class values  “Jitter” option to deal with nominal attributes (and to detect “hidden” data points)  “Zoom-in” function 34

35 Data Visualization in WEKA

36 Data Visualization in WEKA

37 Data Visualization in WEKA

38 Data Visualization in WEKA

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40 Data Visualization in WEKA

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The Mystery Sound  And what would this be? 44