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 ...
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Recent years have brought significant advances to Natural languageprocessing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in this application domain are skill extra...
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In this work, we present a model for document-grounded response generation in dialog that is decomposed into two components according to Bayes' theorem. One component is a traditional ungrounded response generatio...
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The Barabási-Albert-model is commonly used to generate scale-free graphs, like social networks. To generate dynamics in these networks, methods for altering such graphs are needed. Growing and shrinking is done s...
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The Barabási-Albert-model is commonly used to generate scale-free graphs, like social networks. To generate dynamics in these networks, methods for altering such graphs are needed. Growing and shrinking is done simply by doing further generation iterations or undo them. In our paper we present four methods to merge two graphs based on the Barabási-Albert-model, and five strategies to reverse them. First we compared these algorithms by edge preservation, which describes the ratio of the inner structure kept after altering. To check if hubs in the initial graphs are hubs in the resulting graphs as well, we used the node-degree rank correlation. Finally we tested how well the node-degree distribution follows the power-law function from the Barabási-Albert-model.
Although probabilistic networks and fuzzy clustering may seem to be disparate areas of research, they can both be seen as generalizations of naive Bayes classifiers. If all descriptive attributes are numeric, naive Ba...
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Although probabilistic networks and fuzzy clustering may seem to be disparate areas of research, they can both be seen as generalizations of naive Bayes classifiers. If all descriptive attributes are numeric, naive Bayes classifiers often assume an axis-parallel multidimensional normal distribution for each class. Probabilistic networks remove the requirement that the distributions must be axis-parallel by taking covariances into account where this is necessary. Fuzzy clustering tries to find general or axis-parallel distributions to cluster the data. Although it neglects the classification information, it can be used to improve the result of the above mentioned methods by removing the restriction to only one distribution per classification.
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.
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.
In fuzzy clustering soft cluster partitions are formed based on the similarity of data points to the respective cluster prototypes. Similarity is defined in terms of simultaneous closeness regarding all attributes. In...
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In fuzzy clustering soft cluster partitions are formed based on the similarity of data points to the respective cluster prototypes. Similarity is defined in terms of simultaneous closeness regarding all attributes. In some applications the values of many attributes have been measured, but a natural clustering, if it exists, occurs within a (small) subset of attributes. The remaining dimensions can be considered irrelevant. They can obscure an existing grouping and make it harder to discover the cluster structure. In probabilistic fuzzy clustering irrelevant attributes can lead to coincidental cluster centers in the worst case. We study this effect in detail as well as the robustness of different similarity functions and their possible parameterizations against irrelevant input dimensions. Empirical evidence is given for the different properties of the membership functions
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