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Basic Customer Segmentation

ClusteringK-MeansCustomer segmentationMy folder: D:\data\OneDrive\Documents\knime-workspace\Example Workflows\Customer Intelligence\Customer SegmentationManhattan distance
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Mar 9, 2016 9:39 AM
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This workflow implements a basic customer segmentation through a clustering procedure. The data is of a telecommunication company; its customer records. Feature selection has been done in an another workflow by drawing boxplots and densityplots. Those fearures have been selected which show good relationship with the target, churn. Eight features are considered. Weka widget is used for clustering. Three clusters can be clearly seen in the 2D scatterplot as also 3D scatterplot. To normalize any one of the three techniques can be considered. Clusters remain unaffected. Also distance measure can be either Euclidean or Manhattan but again the three clusters remain unaffected. Simple workflow to draw screeplot is also shown.

External resources

  • Cluster Analysis--Wikipedia
  • Cluster Analysis--Basics
  • Proximity Measures
  • Recent Advances in Clustering--A Survey
  • Notes on Kmeans algorithm on Moodle
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Used extensions & nodes

Created with KNIME Analytics Platform version 4.6.3
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    Erlwood Knime Open Source Core

    Erlwood

    Version 4.0.0

    erlwood_cheminf
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    KNIME Base nodesTrusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.0.2, 4.1.0, 4.6.2

    knime
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    KNIME Excel SupportTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

    knime
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    KNIME JavaScript ViewsTrusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.0.2, 4.1.0

    knime
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    KNIME PlotlyTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

    knime
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    KNIME Weka Data Mining Integration (3.7)Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

    knime
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    MMI Data Analytics Nodes

    MMI Agency

    Version 1.0.100

    mmiagency

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