KNIME Essentials


KNIME Essentials
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Overview
Table of Contents
Author
Support
Sample Chapters
  • Learn the essentials of KNIME, from importing data to data visualization and reporting
  • Utilize a wide range of data processing solutions
  • Visualize your final data sets using KNIME’s powerful data visualization options

Book Details

Language : English
Paperback : 148 pages [ 235mm x 191mm ]
Release Date : October 2013
ISBN : 1849699216
ISBN 13 : 9781849699211
Author(s) : Gábor Bakos
Topics and Technologies : All Books, Big Data and Business Intelligence, Data, Java

Table of Contents

Preface
Chapter 1: Installing and Using KNIME
Chapter 2: Data Preprocessing
Chapter 3: Data Exploration
Chapter 4: Reporting
Index
  • Chapter 1: Installing and Using KNIME
    • Few words about KNIME
    • Installing KNIME
      • Installation using the archive
        • KNIME for Windows
        • KNIME for Linux
        • KNIME for Mac OS X
      • Troubleshooting
    • KNIME terminologies
      • Organizing your work
      • Nodes
        • Node lifecycle
      • Meta nodes
      • Ports
        • Data tables
        • Port view
      • Flow variables
      • Node views
        • HiLite
      • Eclipse concepts
        • Preferences
        • Logging
    • User interface
      • Getting started
      • Setting preferences
        • KNIME
        • Other preferences
      • Installing extensions
      • Workbench
        • Workflow handling
        • Node controls
        • Meta nodes
        • Workflow lifecycle
        • Other views
    • Summary
    • Chapter 2: Data Preprocessing
      • Importing data
        • Importing data from a database
          • Starting Java DB
        • Importing data from tabular files
        • Importing data from web services
          • REST services
        • Importing XML files
        • Importing models
        • Other formats
        • Public data sources
      • Regular expressions
        • Basic syntax
        • Partial versus whole match
        • Usage from Java
        • References and tools
        • Alternative pattern description
      • Transforming the shape
        • Filtering rows
          • Sampling
        • Appending tables
        • Less columns
          • Dimension reduction
        • More columns
        • GroupBy
        • Pivoting and Unpivoting
        • One2Many and Many2One
        • Cosmetic transformations
          • Renames
          • Changing the column order
          • Reordering the rows
          • The row ID
        • Transpose
      • Transforming values
        • Generic transformations
          • Java snippets
          • The Math Formula node
        • Conversion between types
          • Binning
        • Normalization
          • Text normalization
        • Multiple columns
        • XML transformation
        • Time transformation
        • Smoothing
      • Data generation
        • Generating the grid
      • Constraints
      • Loops
      • Workflow customization
      • Case study – finding min-max in the next n rows
      • Case study – ranks within groups
      • Summary
      • Chapter 3: Data Exploration
        • Computing statistics
        • Overview of visualizations
        • Visual guide for the views
        • Distance matrix
        • Using visual properties
          • Color
          • Size
          • Shape
        • KNIME views
          • HiLite
            • Use cases for HiLite
          • Row IDs
          • Extreme values
        • Basic KNIME views
          • The Box plots
          • Hierarchical clustering
          • Histograms
          • Interactive Table
          • The Lift chart
          • Lines
          • Pie charts
          • The Scatter plots
          • Spark Line Appender
          • Radar Plot Appender
          • The Scorer views
        • JFreeChart
          • The Bar charts
          • The Bubble chart
          • Heatmap
          • The Histogram chart
          • The Interval chart
          • The Line chart
          • The Pie chart
          • The Scatter plot
        • Open Street Map
        • 3D Scatterplot
        • Other visualization nodes
          • The R plot, Python plot, and Matlab plot
          • The official R plots
          • The RapidMiner view
          • The HiTS visualization
        • Tips for HiLiting
          • Using Interactive HiLite Collector
          • Finding connections
        • Visualizing models
          • Further ideas
        • Summary
        • Chapter 4: Reporting
          • Installation of the reporting extensions
          • Reporting concepts
          • Importing data
            • Sending data and images to a report
            • Importing from other sources
            • Joining data sets
          • Preferences
          • Using the designer
            • In visible views
            • Report properties
            • Report items
              • Label
              • Text
              • Dynamic text
              • Data
              • Image
              • Grid
              • List
              • Table
              • Chart
              • Cross Tab
            • Quick Tools
              • Aggregation
              • Relative time period
          • Generating reports
          • Using colors
          • Using HiLite
          • Using workflow variables
          • Suggested readings
          • Summary

          Gábor Bakos

          Gábor Bakos is a programmer and a mathematician, having a few years of experience with KNIME and KNIME node development (HiTS nodes and RapidMiner integration for KNIME). In Trinity College, Dublin, the author was helping a research group with his data analysis skills (also had the opportunity to improve those), and with the new KNIME node development. When he worked for the evopro Kft. or the Scriptum Informatika Zrt., he was also working on various data analysis software products. He currently works for his own company, Mind Eratosthenes Kft. (www.mind-era.com), where he develops the RapidMiner integration for KNIME (tech.knime.org/community/rapidminer-integration), among other things.
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          Submit Errata

          Please let us know if you have found any errors not listed on this list by completing our errata submission form. Our editors will check them and add them to this list. Thank you.


          Errata

          - 1 submitted: last submission 09 Jan 2014

          On page number 43 :

          The following sentence is incorrect:
          "If you would like to join two tables based on the row indices
          (practically combine them in a new table horizontally), you should
          use the Column Appender node."

          Unfortunately the Column Appender works using the row keys, not row
          indices, so this sentence is wrong. Use the following sentence as an alternative:

          "When your data is ordered the same way for both tables' row keys,
          you can use Column Appender for a more efficient join."

          See the following discussion in the KNIME forum for details:
          http://tech.knime.org/forum/knime-general/column-appender-useless

          And the current node description:
          http://www.knime.org/files/nodedetails/_manipulation_column_column_split_combine_Column_Appender.html

           

          Sample chapters

          You can view our sample chapters and prefaces of this title on PacktLib or download sample chapters in PDF format.

          Frequently bought together

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          What you will learn from this book

          • Install and configure KNIME
          • Create KNIME workflows and report templates
          • Import data to KNIME
          • Transform data with KNIME workflows
          • Enhance your data with data from other sources
          • Visualize data using KNIME
          • Generate reports from your data

          In Detail

          KNIME is an open source data analytics, reporting, and integration platform, which allows you to analyze a small or large amount of data without having to reach out to programming languages like R.

          "KNIME Essentials" teaches you all you need to know to start processing your first data sets using KNIME. It covers topics like installation, data processing, and data visualization including the KNIME reporting features. Data processing forms a fundamental part of KNIME, and KNIME Essentials ensures that you are fully comfortable with this aspect of KNIME before showing you how to visualize this data and generate reports.

          "KNIME Essentials" guides you through the process of the installation of KNIME through to the generation of reports based on data. The main parts between these two phases are the data processing and the visualization. The KNIME variants of data analysis concepts are introduced, and after the configuration and installation description comes the data processing which has many options to convert or extend it. Visualization makes it easier to get an overview for parts of the data, while reporting offers a way to summarize them in a nice way.

          Approach

          "KNIME Essentials" is a practical guide aimed at getting the results you want, as quickly as possible.

          Who this book is for

          "Knime Essentials" is written for data analysts looking to quickly get up to speed using the market leader in data processing tools, KNIME. No knowledge of KNIME is required, but we will assume that you have some background in data processing.

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