Hub
Pricing About
WorkflowWorkflow

Telecom Churn--Demonstrating kmeans++ effectiveness

KmeansKmeans++
Draft Latest edits on 
Feb 28, 2021 4:40 AM
Drag & drop
Like
Download workflow
Workflow preview
The example shows effectiveness of kmeans++ towards creating good clusters. When kmeans++ is used in selecting initial seed Centroids (through Weka's SimpleKMeans widget) the max Silhoutte Coeff for a data pt is 0.659 and min is 0.301. But when we use k-means widget with Random initialization of Centroids, the Silhoutte Coeff vary from 0.386 to -0.0.39.
Loading deploymentsLoading ad hoc jobs

Used extensions & nodes

Created with KNIME Analytics Platform version 4.3.1
  • Go to item
    Erlwood Knime Open Source Core

    Erlwood

    Version 4.0.0

    erlwood_cheminf
  • Go to item
    KNIME Base nodesTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.1

    knime
  • Go to item
    KNIME Distance MatrixTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

    knime
  • Go to item
    KNIME Excel SupportTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.1

    knime
  • Go to item
    KNIME PlotlyTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

    knime
  • Go to item
    KNIME Weka Data Mining Integration (3.7)Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.1

    knime

Legal

By using or downloading the workflow, you agree to our terms and conditions.

KNIME
Open for Innovation

KNIME AG
Talacker 50
8001 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • Courses + Certification
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more about KNIME Business Hub
© 2025 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Data Processing Agreement
  • Credits