Hub
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
  • KNIME Hub
  • Nodes
  • Spark PCA
NodeNode / Manipulator

Spark PCA

Tools & Services Apache Spark Mining Dimensionality Reduction
Drag & drop
Like
Copy short link

This node performs a principal component analysis (PCA) on the given data using the Apache Spark implementation . The input data is projected from its original feature space into a space of (possibly) lower dimension with a minimum of information loss.

Node details

Input ports
  1. Type: Spark Data
    Spark DataFrame/RDD
    Input Spark DataFrame/RDD
Output ports
  1. Type: Spark Data
    Projected Input DataFrame/RDD
    The input DataFrame/RDD projected onto the principal components. Input columns that were not included in the principal component analysis are retained.
  2. Type: Spark Data
    Principal Component Matrix
    A DataFrame/RDD with the principal components matrix.

Extension

The Spark PCA node is part of this extension:

  1. Go to item

Related workflows & nodes

  1. Go to item
    Local big data Irish meter
    Hive Spark Spark PCA
    +7
    This workflow uses a portion of the Irish Energy Meter dataset, and presents a simple ana…
    knime > Examples > 10_Big_Data > 02_Spark_Executor > 09_Big_Data_Irish_Meter_on_Spark_only
  2. Go to item
    Local big data Irish meter
    Hive Spark Spark PCA
    +7
    This workflow uses a portion of the Irish Energy Meter dataset, and presents a simple ana…
    knime > Education > Courses > L4-BD Introduction to Big Data with KNIME Analytics Platform > 3_Spark > 4_Examples > 09_Big_Data_Irish_Meter_on_Spark_only
  1. Go to item
  2. Go to item
  3. Go to item
  4. Go to item
  5. Go to item
  6. Go to item

KNIME
Open for Innovation

KNIME AG
Hardturmstrasse 66
8005 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • E-Learning course
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • KNIME Open Source Story
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more on KNIME Server
© 2022 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Credits