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)

Visual guide for the views


In this section, we will introduce the iris dataset (Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository (http://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science. Iris dataset: http://archive.ics.uci.edu/ml/datasets/Iris) with some screenshots from the views (without their controls).

Box plot for the numeric columns

The Conditional Box Plot and the Box Plot nodes' views look similar. These are also sometimes called box-and-whisker diagrams. The Box Plot node visualizes the values of different columns, while the Conditional Box Plot view shows one column's values grouped by a nominal column's values. As you can see in the screenshot, the HiLite information is visible for the outliers (but only for those values). You can also select the outliers and HiLite them.

The shape of the outlier points is not influenced by the shape property.

Histogram with a few columns selected, HiLited rows and colored...