Fuzzy cluster analysis is a method for unsupervised clustering. However sometimes class information is available for the given dataset, i.e., only the number of clusters per class is unknown. In this paper it is discu...
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Fuzzy cluster analysis is a method for unsupervised clustering. However sometimes class information is available for the given dataset, i.e., only the number of clusters per class is unknown. In this paper it is discussed how class information can be exploited. Some common approaches are reviewed and a new approach is suggested, which integrates class information into fuzzy cluster analysis.
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 model...
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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.
In this paper we consider the problem of inducing causal relations from statistical data. Although it is well known that a correlation does not justify the claim of a causal relation between two measures, the question...
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Naive Bayes classifiers are a well-known and powerful type of classifier that can easily be induced from a dataset of sample cases. However, the strong conditional independence and distribution assumptions underlying ...
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Naive Bayes classifiers are a well-known and powerful type of classifier that can easily be induced from a dataset of sample cases. However, the strong conditional independence and distribution assumptions underlying them can sometimes lead to poor classification performance. Another prominent type of classifier are neuro-fuzzy classification systems which derive (fuzzy) classifiers from data using neural network inspired learning methods. Since there are certain structural similarities between a neuro-fuzzy classifier and a naive Bayes classifier, the idea suggests itself to mapping the latter to the former in order to improve its capabilities.
作者：
R. KruseC. BorgeltD. NauckRudolf Kruse
Christian Borgelt and Detlef Nauck Department of Knowledge Processing and Language Engineering Otto-von-Guericke University of Magdeburg Magdeburg Germany
In meeting the challenges that resulted from the explosion of collected, stored, and transferred data, knowledge discovery in databases or data mining has emerged as a new research area. However, the approaches studie...
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In meeting the challenges that resulted from the explosion of collected, stored, and transferred data, knowledge discovery in databases or data mining has emerged as a new research area. However, the approaches studied in this area have mainly been oriented at highly structured and precise data. In addition, the goal to obtain understandable results is often neglected. Therefore we suggest to concentrate on information mining, i.e., the analysis of heterogeneous information sources with the prominent aim of producing comprehensible results. Since the aim of fuzzy technology has always been to model linguistic information and to achieve understandable solutions, we expect it to play an important role in information mining.
Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralque...
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ISBN:
(数字)9783540476986
ISBN:
(纸本)9783540476979
Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralquestion is whether and to what extent the meaning extracted from the data is expressed in a formal way that allows not only humans but also machines to understand and re-use it, i. e. , whether the semantics are formal semantics. Conversely, the input to KD processes di?ers between KD projects and approaches. One central questioniswhetherthebackgroundknowledge,businessunderstanding,etc. that the analyst employs to improve the results of KD is a set of natural-language statements, a theory in a formal language, or somewhere in between. Also, the data that are being mined can be more or less structured and/or accompanied by formal semantics. These questions must be asked in every KD e?ort. Nowhere may they be more pertinent, however, than in KD from Web data (“Web mining”). Thisis due especially to the vast amounts and heterogeneity of data and ba- ground knowledge available for Web mining (content, link structure, and - age), and to the re-use of background knowledge and KD results over the Web as a global knowledge repository and activity space. In addition, the (Sem- tic) Web can serve as a publishing space for the results of knowledge discovery from other resources, especially if the whole process is underpinned by common ontologies.
We are glad to present the proceedings of the 5th biennial conference in the Intelligent Data Analysis series. The conference took place in Berlin, Germany, August 28–30, 2003. IDA has by now clearly grown up. Starte...
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ISBN:
(数字)9783540452317
ISBN:
(纸本)9783540408130
We are glad to present the proceedings of the 5th biennial conference in the Intelligent Data Analysis series. The conference took place in Berlin, Germany, August 28–30, 2003. IDA has by now clearly grown up. Started as a small si- symposium of a larger conference in 1995 in Baden-Baden (Germany) it quickly attractedmoreinterest(bothsubmission-andattendance-wise),andmovedfrom London (1997) to Amsterdam (1999), and two years ago to Lisbon. Submission ratesalongwiththeeverimprovingqualityofpapershaveenabledtheor- nizers to assemble increasingly consistent and high-quality programs. This year we were again overwhelmed by yet another record-breaking submission rate of 180 papers. At the Program Chairs meeting we were – based on roughly 500 reviews – in the lucky position of carefully selecting 17 papers for oral and 42 for poster presentation. Poster presenters were given the opportunity to summarize their papers in 3-minute spotlight presentations. The oral, spotlight and poster presentations were then scheduled in a single-track, 2. 5-day conference program, summarized in this book. In accordance with the goal of IDA, “to bring together researchers from diverse disciplines,” we achieved a nice balance of presentations from the more theoreticalside(bothstatisticsandcomputerscience)aswellasmoreapplicati- oriented areas that illustrate how these techniques can be used in practice. Work presented in these proceedings ranges from theoretical contributions dealing, for example, with data cleaning and compression all the way to papers addressing practical problems in the areas of text classi?cation and sales-rate predictions. A considerable number of papers also center around the currently so popular applications in bioinformatics.
The International Conference on Information processing and Management of - certainty in knowledge-Based Systems, IPMU, is organized every two years with the aim of bringing together scientists working on methods for t...
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ISBN:
(数字)9783642140556
ISBN:
(纸本)9783642140549
The International Conference on Information processing and Management of - certainty in knowledge-Based Systems, IPMU, is organized every two years with the aim of bringing together scientists working on methods for the management of uncertainty and aggregation of information in intelligent systems. Since 1986, this conference has been providing a forum for the exchange of ideas between th theoreticians and practitioners working in these areas and related ?elds. The 13 IPMU conference took place in Dortmund, Germany, June 28–July 2, 2010. This volume contains 79 papers selected through a rigorous reviewing process. The contributions re?ect the richness of research on topics within the scope of the conference and represent several important developments, speci?cally focused on theoretical foundations and methods for information processing and management of uncertainty in knowledge-based systems. We were delighted that Melanie Mitchell (Portland State University, USA), Nihkil R. Pal (Indian Statistical Institute), Bernhard Sch¨ olkopf (Max Planck I- titute for Biological Cybernetics, Tubing ¨ en, Germany) and Wolfgang Wahlster (German Research Center for Arti?cial Intelligence, Saarbruc ¨ ken) accepted our invitations to present keynote lectures. Jim Bezdek received the Kamp´ede F´ eriet Award, granted every two years on the occasion of the IPMU conference, in view of his eminent research contributions to the handling of uncertainty in clustering, data analysis and pattern recognition.
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