KNIME - the Konstanz information miner-Reference-Cited by-同舟云学术

KNIME - the Konstanz information miner

Published:2009-11-16 Issue:1 Volume:11 Page:26-31
ISSN:1931-0145
Container-title:ACM SIGKDD Explorations Newsletter
language:en
Short-container-title:SIGKDD Explor. Newsl.

Author:

Berthold Michael R.¹,Cebron Nicolas¹,Dill Fabian¹,Gabriel Thomas R.¹,Kötter Tobias¹,Meinl Thorsten¹,Ohl Peter¹,Thiel Kilian¹,Wiswedel Bernd¹

Affiliation:

1. University of Konstanz, Konstanz, Germany

Abstract

The Konstanz Information Miner is a modular environment, which enables easy visual assembly and interactive execution of a data pipeline. It is designed as a teaching, research and collaboration platform, which enables simple integration of new algorithms and tools as well as data manipulation or visualization methods in the form of new modules or nodes. In this paper we describe some of the design aspects of the underlying architecture, briey sketch how new nodes can be incorporated, and highlight some of the new features of version 2.0.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/1656274.1656280

Reference13 articles.

1. BIRT. Business Intelligence and Reporting Tools. http://www.eclipse.org/birt/. BIRT. Business Intelligence and Reporting Tools. http://www.eclipse.org/birt/.

2. Data Mining Group. Predictive Model Markup Language (PMML). http://www.dmg.org/. Data Mining Group. Predictive Model Markup Language (PMML). http://www.dmg.org/.

3. R Project. The R Project for Statistical Computing. http://www.r-project.org/. R Project. The R Project for Statistical Computing. http://www.r-project.org/.

4. Ian H. Witten and Eibe Frank (2005). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann San Francisco 2nd edition. Ian H. Witten and Eibe Frank (2005). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann San Francisco 2nd edition.

Cited by 1567 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation;Computational and Structural Biotechnology Journal;2024-12

2. Structural analysis and shape-based identification of novel inhibitors targeting the Trypanosoma cruzi proteasome;International Journal of Biological Macromolecules;2024-10

3. Montane peatland response to drought: Evidence from multispectral and thermal UAS monitoring;Ecological Indicators;2024-10

4. Tuning RXR Modulators for PGC1α Recruitment;Journal of Medicinal Chemistry;2024-09-11

5. A machine learning approach to classifying sustainability practices in hotel management;Journal of Sustainable Tourism;2024-09-03