Introduction to KNIME

Frank Acito
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Frank Acito: Indiana University

Chapter Chapter 3 in Predictive Analytics with KNIME, 2023, pp 21-52 from Springer

Abstract: Abstract This chapter of this book introduces the KNIME analytics and data mining tool, a comprehensive platform that offers an intuitive drag-and-drop workflow canvas for data analysis. KNIME serves professional data analysts and beginners with its user-friendly interface, making it an excellent choice for low or no-code predictive analytics and data mining tasks. The chapter covers various aspects of KNIME, starting with its features, which include a vast array of nodes for data connections, transformations, machine learning, and visualization. KNIME is extensible and can run R or Python scripts to enhance its capabilities, and it also integrates features from other analytic platforms like H2O and WEKA. The chapter explains the KNIME Workbench, which is the main interface for creating workflows. It includes components like KNIME Explorer, Workflow Coach, Node Repository, Workflow Editor, Outline, and Console. The Workbench allows users to construct and visualize their analyses step-by-step. The chapter provides information about various learning resources, including courses, documentation, and videos that can users learn KNIME. Users can access free self-paced courses covering different levels of expertise, enabling them to become proficient in using KNIME for various data analysis tasks. Additionally, the chapter demonstrates how to use flow variables to pass information between nodes and how to use loops to iterate over values in a workflow. The chapter introduces the concepts of Metanodes and Components to organize and simplify complex workflows, making them more manageable and self-contained. Overall, the chapter serves as an informative and practical introduction to KNIME, highlighting its key features, resources for learning, and essential tools for workflow organization and analysis. Readers are encouraged to install KNIME and explore its capabilities through hands-on practice to gain proficiency in this powerful data analytics tool.

Date: 2023
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DOI: 10.1007/978-3-031-45630-5_3

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