Thomas R. Gabriel

KNIME: The Konstanz Information Miner

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 and briefly sketch how new nodes can be incorporated.

KNIME - the Konstanz information miner: version 2.0 and beyond

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.

Influence of fuzzy norms and other heuristics on “Mixed fuzzy rule formation”

In Mixed Fuzzy Rule Formation [Int. J. Approx. Reason. 32 (2003) 67] a method to extract mixed fuzzy rules from data was introduced. The underlying algorithm’s performance is influenced by the choice of fuzzy t-norm and t-conorm, and a heuristic to avoid conflicts between patterns and rules of different classes throughout training. In the following addendum to [Int. J. Approx. Reason. 32 (2003) 67], we discuss in more depth how these parameters affect the generalization performance of the resulting fuzzy rule models.

Fuzzy logic in KNIME - modules for approximate reasoning

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.

Missing Values in Fuzzy Rule Induction

In this paper, we show how an existing fuzzy rule induction algorithm can incorporate missing values in the training procedure in a very natural way. The underlying algorithm generates rules which restrict the feature space only along a few, important attributes. This property can be used to limit the algorithms three major steps to the reduced feature space for each training instance, which allows the features for which no values are known to be ignored. Hence no replacement is necessary and the algorithm simply uses all available knowledge from each training instance. We demonstrate on data sets from the UCI repository that this method works well, generates rule sets that have comparable classification accuracy, and are, at times, even smaller than the rule sets generated by the original algorithm

Formation of hierarchical fuzzy rule systems

Many fuzzy rule induction algorithms have been proposed in the past. Most of them tend to generate too many rides during the learning process. This is due to data sets obtained from real world systems containing distorted elements or noisy data. Most approaches try to completely ignore outliers, which can be potentially harmful since the example may describe a rare but still extremely interesting phenomena in the data. In order to avoid this conflict, we propose to build a hierarchy of fuzzy rule systems. The goal of this model-hierarchy are interpretable models with only few relevant rules on each level of the hierarchy. The resulting fuzzy model hierarchy forms a structure in which the top model covers all data explicitly and generates a significant smaller number of rules than the original fuzzy rule learner. The models on the bottom, on the other hand, consist of only a few rules in each level and explain pans with only weak relevance in the data. We demonstrate the proposed methods usefulness on several classification benchmark data sets. The results demonstrate how the rule hierarchy allows to build much smaller fuzzy rule systems and how the model-especially at higher levels of the hierarchy-remains interpretable.

Constructing Hierarchical Rule Systems

Rule systems have failed to attract much interest in large data analysis problems because they tend to be too simplistic to be use- ful or consist of too many rules for human interpretation. We present a method that constructs a hierarchical rule system, with only a small number of rules at each stage of the hierarchy. Lower levels in this hi- erarchy focus on outliers or areas of the feature space where only weak evidence for a rule was found in the data. Rules further up, at higher lev- els of the hierarchy, describe increasingly general and strongly supported aspects of the data. We demonstrate the proposed methods usefulness on several classification benchmark data sets using a fuzzy rule induction process as the underlying learning algorithm. The results demonstrate how the rule hierarchy allows to build much smaller rule systems and how the model—especially at higher levels of the hierarchy—remains in- terpretable. The presented method can be applied to a variety of local learning systems in a similar fashion.

chemalot and chemalot_knime: Command line programs as workflow tools for drug discovery

BackgroundAnalyzing files containing chemical information is at the core of cheminformatics. Each analysis may require a unique workflow. This paper describes the chemalot and chemalot_knime open source packages. Chemalot is a set of command line programs with a wide range of functionalities for cheminformatics. The chemalot_knime package allows command line programs that read and write SD files from stdin and to stdout to be wrapped into KNIME nodes. The combination of chemalot and chemalot_knime not only facilitates the compilation and maintenance of sequences of command line programs but also allows KNIME workflows to take advantage of the compute power of a LINUX cluster.ResultsUse of the command line programs is demonstrated in three different workflow examples: (1) A workflow to create a data file with project-relevant data for structure–activity or property analysis and other type of investigations, (2) The creation of a quantitative structure–property-relationship model using the command line programs via KNIME nodes, and (3) The analysis of strain energy in small molecule ligand conformations from the Protein Data Bank database.ConclusionsThe chemalot and chemalot_knime packages provide lightweight and powerful tools for many tasks in cheminformatics. They are easily integrated with other open source and commercial command line tools and can be combined to build new and even more powerful tools. The chemalot_knime package facilitates the generation and maintenance of user-defined command line workflows, taking advantage of the graphical design capabilities in KNIME.Graphical abstractExample KNIME workflow with chemalot nodes and the corresponding command line pipe

Exploring hierarchical rule systems in parallel coordinates

Rule systems have failed to attract much interest in large data analysis problems because they tend to be too simplistic to be useful or consist of too many rules for human interpretation. We recently presented a method that constructs a hierarchical rule system, with only a small number of rules at each level of the hierarchy. Lower levels in this hierarchy focus on outliers or areas of the feature space where only weak evidence for a rule was found in the data. Rules further up, at higher levels of the hierarchy, describe increasingly general and strongly supported aspects of the data. In this paper we show how a connected set of parallel coordinate displays can be used to visually explore this hierarchy of rule systems and allows an intuitive mechanism to zoom in and out of the underlying model.

Corrigendum to: Influence of fuzzy norms and other heuristics on “Mixed fuzzy rule formation” [Int. J. Approx. Reason. 35 (2004) 195--202]

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