Fuzzy Logic in KNIME - Modules for Approximate Reasoning -

  title={Fuzzy Logic in KNIME - Modules for Approximate Reasoning -},
  author={Michael R. Berthold and Bernd Wiswedel and Thomas R. Gabriel},
  journal={Int. J. Comput. Intell. Syst.},
In this paper we describe the open source data analytics platform KNIME, focusing particularly on extensions and modules supporting fuzzy sets and fuzzy learning algorithms such as fuzzy clustering algorithms, rule induction methods, and interactive clustering tools. In addition we outline a number of experimental extensions, which are not yet part of the open source release and present two illustrative examples from real world applications to demonstrate the power of the KNIME extensions. 
A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects
An overview of freely available and open-source fuzzy systems software is presented in order to provide a well-established framework that helps researchers to find existing proposals easily and to develop well-founded future work.
Project Control and Computational Intelligence: Trends and Challenges
The predicted necessity of constructing new models and IT tools for project control which integrate machine learning-based approaches and treatment of imprecision, vagueness or uncertainty in the information, using key performance indicators linked to fundamental knowledge areas are studied.
Fuzzy rule-based systems for recognition-intensive classification in granular computing context
The experimental results show that the fuzzy approach can not only be used as an alternative one to the probabilistic approaches but also is capable to capture more patterns which probabilism approaches cannot achieve.
Fuzzy Classification Through Generative Multi-task Learning
This chapter introduces the concepts of both generative learning and multi- task learning, and presents a proposed fuzzy approach for multi-task classification, in comparison with traditional classification in the context of discriminative single-task learning.
KNIME an Open Source Solution for Predictive Analytics in the Geosciences [Software and Data Sets]
  • L. Feltrin
  • Computer Science, Geology
    IEEE Geoscience and Remote Sensing Magazine
  • 2015
KNIME (Konstanz Information Miner) is an open source predictive analytics platform suited to process a variety of data formats, from basic csv or xlsx files, to more complex data structures such as xml, url and relational databases.
Modeling of discrete questionnaire data with dimension reduction
The main contribution of the paper is a three-phase algorithm of the discrete model dimension reduction, which allows to model high-dimensional questionnaire data with high number of explanatory variables and their possible realizations.
Using Clustered Heat Maps in Mineral Exploration to Visualize Volcanic-Hosted Massive Sulfide Alteration and Mineralization
This study proposes an extension of a visualization approach common in biochemistry (the clustered heat maps—CHMs) to geochemical data with the main objective of detecting hydrothermal alteration and
Investigating Classification Techniques with Feature Selection For Intention Mining From Twitter Feed
This paper investigates the problem of selecting features that affect extracting user's intention from Twitter feeds based on text mining techniques, and presents two techniques of feature selection followed by classification.
Transformation of discriminative single-task classification into generative multi-task classification in machine learning context
A fundamentally different type of classification is proposed in which the membership of an instance to all classes(/labels) is judged by a multiple-input-multiple-output classifier through generative multi-task learning.


Visualizing high dimensional fuzzy rules
  • M. Berthold, R. Holve
  • Computer Science
    PeachFuzz 2000. 19th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.00TH8500)
  • 2000
An approach to visualize a potentially high-dimensional and large number of (fuzzy) rules in two dimensions using a gradient descent based algorithm to generate a 2D-view which minimizes the error on the pair-wise fuzzy distances between all rules.
Mixed fuzzy rule formation
Advances in Fuzzy Clustering and its Applications
A comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering, addressing timely and relevant concepts and methods whilst identifying major challenges and recent developments in the area.
Applied Fuzzy Arithmetic: An Introduction with Engineering Applications
A well-structured compendium that offers both a deeper knowledge about the theory of fuzzy arithmetic and an extensive view on its applications in the engineering sciences making it useful for graduate courses, researchers and engineers.
Characterization and detection of noise in clustering
  • R. Davé
  • Computer Science
    Pattern Recognit. Lett.
  • 1991
Interactive Exploration of Fuzzy Clusters
This chapter describes methods that assist the user to visually explore fuzzy clusters, a supervised approach to generate clusters for classes of interest of a given data set using local, one-dimensional neighborhood models.
Interactive exploration of fuzzy clusters using neighborgrams
This work describes an interactive method to generate a set of fuzzy clusters for classes of interest of a given, labeled data set and demonstrates the performance of the underlying algorithm on several data sets from the StatLog project.
KNIME: The Konstanz Information Miner
Some of the design aspects of the underlying architecture of the Konstanz Information Miner are described and briefly sketch how new nodes can be incorporated.
PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics
The aim of this book is to present PMML from a practical perspective and is intended for data mining movers and shakers: anyone interested in moving predictive analytic solutions between applications, including students and scientists.