Book Image

KNIME Essentials

By : Gábor Bakos
Book Image

KNIME Essentials

By: Gábor Bakos

Overview of this book

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.
Table of Contents (11 chapters)

Distance matrix


The distance matrix is used not just for visualization, but for learning algorithms too. You can think of them as a column of collections, where each cell contains the difference between the previous rows.

The supported distance functions are the following:

  • Real distances

    • Euclidean()
    • Manhattan ()
    • Cosine ()
  • Bitvector distances

    • Tanimoto ()
    • Dice ()
    • Bitvector cosine ()
  • Distance vector (assuming you already have a distance vector, you can transform it to a distance matrix when there are row order changes or filtering)

  • Molecule distances (from extensions)

The distance matrix feature can be used together with the hierarchical clustering, which also provides a node to view it; this is the main reason we introduced them in this chapter.

You can generate distances using the Distance Matrix Calculate node (just select the function, the numeric columns, and set the name. The chunk size is just for fine tuning larger tables), but you can also load that information with the Distance Matrix...