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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： C. Borgelt R. Kruse Department of Knowledge Processing and Language Engineering Otto-von-Guericke-Universitaet Magdeburg Magdeburg Germany

A major topic of recent research in graphical models has been to develop algorithms to learn them from a dataset of sample cases. However, most of these algorithms do not take into account that learned graphical models may be used for time-critical reasoning tasks and that in this case the time complexity of evidence propagation may have to be restricted, even if this can be achieved only by accepting approximations. In this paper we suggest a simulated annealing approach to learn graphical models with hypertree structure, with which the complexity of the popular join tree evidence propagation method can be controlled at learning time by restricting the size of the cliques of the learned network.

关键词： Graphical models Simulated annealing Tree graphs knowledge engineering Costs Bayesian methods Markov random fields Inference algorithms

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Finding the Number of Fuzzy Clusters by Resampling

Finding the Number of Fuzzy Clusters by Resampling

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： C. Borgelt R. Kruse Department of Knowledge Processing and Language Engineering Otto-von-Guericke University of Magdeburg Magdeburg Germany

Recently several papers studied resampling approaches to determine the number of clusters in prototype-based clustering. The core idea underlying these approaches is that with the right choice for the number of clusters basically the same cluster structures should be obtained from subsamples of the given data set, while a wrong choice should produce considerably varying cluster structures. In this paper we investigate whether these approaches can be transferred to fuzzy clustering. It turns out that they are applicable to fuzzy clustering as well, but that not all relative cluster evaluation measures that work for crisp clustering can also be used for fuzzy clustering.

关键词： Clustering algorithms Prototypes Design engineering knowledge engineering Gaussian processes Knee Entropy Stability

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Introduction to the "Soft Computing and Intelligent Data Analysis" Minitrack

Introduction to the "Soft Computing and Intelligent Data Ana...

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Hawaii International Conference on System Sciences

作者： Rudolf Kruse Otto von Guericke University Faculty of Computer Science Department of Knowledge Processing and Language Engineering

ISBN: (纸本)9781479925056

Soft computing refers to a collection of computational techniques in computer science, artificial intelligence, machine learning and some engineering disciplines, which attempt to study, model, and analyze very complex phenomena: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to conventional mathematical and analytical methods. That said, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive. To react quickly and successfully is a matter of knowledge and the task to provide relevant, updated and useful knowledge for management is the arena for developing, building and implementing intelligent support systems.

关键词： Computational modeling Switching systems Electronic countermeasures Predictive models Data analysis Computer science

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A modification to improve possibilistic fuzzy cluster analysis

A modification to improve possibilistic fuzzy cluster analys...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： H. Timm R. Kruse Department of Knowledge Processing and Language Engineering Otto von Guericke University Magdeburg Magdeburg Germany

We explore an approach to possibilistic fuzzy clustering that avoids a severe drawback of the conventional approach, namely that the objective function is truly minimized only if all cluster centers are identical. Our approach is based on the idea that this undesired property can be avoided if we introduce a mutual repulsion of the clusters, so that they are forced away from each other. We develop this approach for the possibilistic fuzzy c-means algorithm and the Gustafson-Kessel algorithm.

关键词： Clustering algorithms Prototypes knowledge engineering Fuzzy sets Humans Zinc Euclidean distance Covariance matrix

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Fuzzy frequent pattern discovering based on recursive elimination

Fuzzy frequent pattern discovering based on recursive elimin...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Xiaomeng Wang C. Borgelt R. Kruse Department of Knowledge Processing and Language Engineering Otto-von-Guericke University of Magdeburg Magdeburg Germany

Real life transaction data often miss some occurrences of items that are actually present. As a consequence some potentially interesting frequent patterns cannot be discovered, since with exact matching the number of supporting transactions may be smaller than the user-specified minimum. In order to allow approximate matching during the mining process, we propose an approach based on transaction editing. Our recursive algorithm relies on a step by step elimination of items from the transaction database together with a recursive processing of transaction subsets. This algorithm works without complicated data structures and allows us to find fuzzy frequent patterns easily.

关键词： Costs Transaction databases Pattern matching Data structures Data engineering knowledge engineering Computer science Delay effects Association rules Fuzzy sets

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Temporal pattern mining

Temporal pattern mining

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International Conference on Signals and Electronic Systems, ICSES

作者： Rudolf Kruse Matthias Steinbrecher Christian Moewes Department of Knowledge Processing and Language Engineering Otto-von-Guericke University of Magdeburg Magdeburg Germany

Data analysis has become an integral part in many economic fields. In this paper, we present several real-world applications occurring in the fields of automobile development and manufacturing, finance, and online communities. The given examples share one aspect in common: time. It is not only the fact to find patterns inside data volumes but also to identify them based on their temporal behaviour. We will give examples of dealing with different models of incorporating the temporal aspects. Furthermore some new results in the area of Visual Data Analysis are presented. These methods offer intuitive methods of guiding the user through the process of data and model inspection and assist in drawing conclusions with the help of meaningful graphical representations.

关键词： Sensors Vehicle crash testing Visualization Three dimensional displays Fuzzy sets Companies Association rules

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Fuzzy clustering of quantitative and qualitative data

Fuzzy clustering of quantitative and qualitative data

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Conference of the North American Fuzzy Information processing Society - NAFIPS

作者： C. Doring C. Borgelt R. Kruse Department of Knowledge Processing and Language Engineering Otto-von-Guericke University of Magdeburg Magdeburg Germany

In many applications the objects to cluster are described by quantitative as well as qualitative features. A variety of algorithms has been proposed for unsupervised classification if fuzzy partitions and descriptive cluster prototypes are desired. However, most of these methods are designed for data sets with variables measured in the same scale type (only categorical, or only metric). We propose a new fuzzy clustering approach based on a probabilistic distance measure. Thus a major drawback of present methods can be avoided which ties in the vulnerability to favor one type of attributes.

关键词： Clustering algorithms Prototypes Partitioning algorithms Data analysis Data engineering Design engineering knowledge engineering Design methodology Frequency Fuzzy sets

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Fuzzy Learning Vector Quantization with Size and Shape Parameters

Fuzzy Learning Vector Quantization with Size and Shape Param...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： C. Borgelt A. Nurnberger R. Kruse Department of Knowledge Processing and Language Engineering Otto-von-Guericke University of Magdeburg Magdeburg Germany

We study an extension of fuzzy learning vector quantization that draws on ideas from the more sophisticated approaches to fuzzy clustering, enabling us to find fuzzy clusters of ellipsoidal shape and differing size with a competitive learning scheme. This approach may be seen as a kind of online fuzzy clustering, which can have advantages w.r.t. the execution time of the clustering algorithm. We demonstrate the usefulness of our approach by applying it to document collections, which are, in general, difficult to cluster due to the high number of dimensions and the special distribution characteristics of the data

关键词： Vector quantization Shape Clustering algorithms Fuzzy sets Covariance matrix Partitioning algorithms knowledge engineering Maximum likelihood estimation Web pages

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Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning

arXiv

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arXiv 2024年

作者： Elshabrawy, Ahmed Huang, Yongxin Gurevych, Iryna Aji, Alham Fikri Department Natural Language Processing MBZUAI United Arab Emirates Ubiquitous Knowledge Processing Lab Technical University of Darmstadt Germany

While Large language Models (LLMs) exhibit remarkable capabilities in zero-shot and few-shot scenarios, they often require computationally prohibitive sizes. Conversely, smaller Masked language Models (MLMs) like BERT and RoBERTa achieve state-of-the-art results through fine-tuning but struggle with extending to few-shot and zero-shot settings due to their architectural constraints. Hence, we propose Statement-Tuning, a technique that models discriminative tasks as a set of finite statements and trains an encoder model to discriminate between the potential statements to determine the label. We do Statement-Tuning on multiple tasks to enable cross-task generalization. Experimental results demonstrate that Statement-Tuning achieves competitive performance compared to state-of-the-art LLMs with significantly fewer parameters. Moreover, the study investigates the impact of several design choices on few-shot and zero-shot generalization, revealing that Statement-Tuning can achieve strong performance with modest training data and benefits from task and statement diversity for unseen task generalizability. We release all the code used to generate statement data, train and evaluate our Statement-Tuned models. © 2024, CC BY.

关键词： Signal encoding

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Hierarchy identification for automatically generating table-of-contents

Hierarchy identification for automatically generating table-...

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9th International Conference on Recent Advances in Natural language processing, RANLP 2013

作者： Erbs, Nicolai Gurevych, Iryna Zesch, Torsten Ubiquitous Knowledge Processing Lab. Department of Computer Science Technische Universität Darmstadt Germany Information Center for Education German Institute for Educational Research and Educational Information Germany Language Technology University of Duisburg-Essen Germany

A table-of-contents (TOC) provides a quick reference to a document's content and structure. We present the first study on identifying the hierarchical structure for automatically generating a TOC using only textual features instead of structural hints e.g. from HTML-tags. We create two new datasets to evaluate our approaches for hierarchy identification. We find that our algorithm performs on a level that is sufficient for a fully automated system. For documents without given segment titles, we extend our work by automatically generating segment titles. We make the datasets and our experimental framework publicly available in order to foster future research in TOC generation.

关键词： Automation

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