When we mine information for knowledge on a whole data streams it's necessary to cope with uncertainty as only a part of the stream is available. We introduce a stastistical technique, independant from the used al...
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Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points which are not sampled will not have their labels. Although there is a straig...
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In the information age, data is pervasive. In some applications, data explosion is a significant phenomenon. the massive data volume poses challenges to both human users and computers. In this project, we propose a ne...
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Assessing the similarity between objects is a prerequisite for many datamining techniques. this paper introduces a novel approach to learn distance functions that maximizes the clustering of objects belonging to the ...
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In this paper there will be presented the new opportunities for applying linguistic algorithms of patternrecognition for computer understanding of image semantic content in intelligent information systems. A successf...
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In this paper there will be presented the new opportunities for applying linguistic algorithms of patternrecognition for computer understanding of image semantic content in intelligent information systems. A successful obtaining of the crucial semantic information of the image - especially medical - may contribute considerably to the creation of new intelligent cognitive information systems. thanks to the new algorithms of cognitive resonance between stream of the data extracted from the image and expectations taken from the representation of the medical knowledge, we can understand the merit content of the image even if the form of the image is very different from any known pattern. It seems that in the near future the technique of automatic understanding of images may become one of the effective tools for semantic interpreting, and intelligent storing of the visual data in scattered databases. In this article we will try proving that structural techniques may be applied in the case of tasks related to automatic classification and machine perception of the semantic meaning of selected classes of medical patterns.
For Pen-input on-line signature verification algorithms, the influence of intersession variability is a considerable problem because hand-written signatures change with time, causing performance degradation. In our pr...
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the area of interest for this paper covers patternrecognition method, which can find and classify all useful relations between data entries in the time series. Genetic algorithm has been deployed to prepare and gover...
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the area of interest for this paper covers patternrecognition method, which can find and classify all useful relations between data entries in the time series. Genetic algorithm has been deployed to prepare and govern a set of independent patterns. For each pattern additional quality value has been added. this value corresponds to the level of certainty and is introduced in the work. Practical application of this solution consists of data fitting and prediction. Analyzed data can be non continuous and incomplete. In uncertain cases algorithm presents either no response at all or more than one answer to processed data. Architecture of the system offers possibility to interleave learning phase with use. Genetic algorithm applied in the method facilitates niche techniques as well as crowd factor and specialized population selection methods. Early testing results, which include prediction and fitting of simple time series with up to 50 percent of missing data, are presented at the end of the paper.
Clustering is a classification process in datamining, very used mainly for grouping of continuous values. the traditional techniques of clustering such as fuzzy C-means clustering (FCM), create groups that don't ...
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Clustering is a classification process in datamining, very used mainly for grouping of continuous values. the traditional techniques of clustering such as fuzzy C-means clustering (FCM), create groups that don't have, many times, practical sense to the user. Relative information gain has been used with success in classification applications, for instance the induction of decision tree. Our goal is to modify the way how the distance is calculated among elements in the FCM algorithm, adding to the calculation the relative information gain. the elements are grouped according to a categorical field selected from the own training dataset. therefore groups are created and induced according to the gain criterion calculated among the elements and the categorical field.
Fuzzy logic has proved to be a powerful tool to represent imprecise and irregular patterns. this paper presents a novel fuzzy approach for recognizing online Persian (Farsi) handwriting which is also useful for multi-...
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Fuzzy logic has proved to be a powerful tool to represent imprecise and irregular patterns. this paper presents a novel fuzzy approach for recognizing online Persian (Farsi) handwriting which is also useful for multi-writer environments. In this approach, the representation of handwriting parameters is accomplished by fuzzy linguistic modeling. the representative features are selected to describe the shape of tokens. Fuzzy linguistic terms provide robustness against handwriting variations. the purposed method was run on a database of Persian isolated handwritten characters and achieved a relatively high recognition rate.
this work suggests an unsupervised fuzzy clustering algorithm based on the concept of participatory learning introduced by Yager in the nineties. the performance of the algorithm is verified with synthetic data sets a...
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this work suggests an unsupervised fuzzy clustering algorithm based on the concept of participatory learning introduced by Yager in the nineties. the performance of the algorithm is verified with synthetic data sets and withthe well-known Iris data. In both circumstances the participatory learning algorithm determines the expected number of clusters and the corresponding cluster centers successfully. Comparisons with Gustafson-Kessel (GK) and modified fuzzy k-means (MFKM) are included to show the effectiveness of the participatory approach in data clustering
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