In this paper we propose a novel data clustering algorithm based on the idea of considering the individual data items as cells belonging to an uni-dimensional cellular automaton. Our proposed algorithm combines insigh...
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ISBN:
(纸本)9783642213441
In this paper we propose a novel data clustering algorithm based on the idea of considering the individual data items as cells belonging to an uni-dimensional cellular automaton. Our proposed algorithm combines insights from both social segregation models based on Cellular Automata theory, where the data items themselves are able to move autonomously in lattices, and also from Ants Clustering algorithms, particularly in the idea of distributing at random the data items to be clustered in lattices. We present a series of experiments with both synthetic and real datasets in order to study empirically the convergence and performance results. these experimental results are compared to the obtained by conventional clustering algorithms.
the paper describes the method of extraction of two-word domain terms combining their features. the features are computed from three sources: the occurrence statistics in a domain-specific text collection, the statist...
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ISBN:
(纸本)9783642217869
the paper describes the method of extraction of two-word domain terms combining their features. the features are computed from three sources: the occurrence statistics in a domain-specific text collection, the statistics of global search engines, and a domain-specific thesaurus. the evaluation of the approach is based on the terminology of manually created thesauri. We show that the use of multiple features considerably improves the automatic extraction of domain-specific terms. We compare the quality of the proposed method in two different domains.
Problem of change detection of remotely sensed images using insufficient labeled patterns is the main topic of present work. Here, semi-supervised learning is integrated with an unsupervised context-sensitive change d...
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ISBN:
(纸本)9783642217869
Problem of change detection of remotely sensed images using insufficient labeled patterns is the main topic of present work. Here, semi-supervised learning is integrated with an unsupervised context-sensitive change detection technique based on modified self-organizing feature map (MSOFM) network. In this method, training of the MSOFM is performed iteratively using unlabeled patterns along with a few labeled patterns. A method has been suggested to select unlabeled patterns for training. To check the effectiveness of the proposed methodology, experiments are carried out on two multitemporal remotely sensed images. Results are found to be encouraging.
this paper investigates the performance of hidden Markov models (HMMs) for handwriting recognition. the Segmental K-Means algorithm is used for updating the transition and observation probabilities, instead of the Bau...
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ISBN:
(纸本)9783642217869
this paper investigates the performance of hidden Markov models (HMMs) for handwriting recognition. the Segmental K-Means algorithm is used for updating the transition and observation probabilities, instead of the Baum-Welch algorithm. Observation probabilities are modelled as multi-variate Gaussian mixture distributions. A deterministic clustering technique is used to estimate the initial parameters of an HMM. Bayesian information criterion (BIC) is used to select the topology of the model. the wavelet transform is used to extract features from a grey-scale image, and avoids binarization of the image.
the proceedings contain 15 papers. the topics discussed include: estimating probability of failure of a complex system based on partial information about subsystems and components, with potential applications to aircr...
the proceedings contain 15 papers. the topics discussed include: estimating probability of failure of a complex system based on partial information about subsystems and components, with potential applications to aircraft maintenance;stepwise feature selection using multiple kernel learning;empirical reconstruction of fuzzy model of experiment in the Euclidean metric;SVM based offline handwritten gurmukhi character recognition;obtaining of a minimal polygonal representation of a curve by means of a fuzzy clustering;KDDClus: a simple method for multi-density clustering;intelligent data mining for turbo-generator predictive maintenance: an approach in real-world;handwritten script identification from a bi-script document at line level using gabor filters;image recognition using kullback-leibler information discrimination;beyond analytical modeling, gathering data to predict real agents' strategic interaction;and construction of enzyme network of arabidopsis thaliana using graph theory.
this paper describes a non-blind, imperceptible and highly robust hybrid Medical Image Watermarking (MIW) technique for a range of medical data management issues. the method simultaneously addresses medical informatio...
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ISBN:
(纸本)9783642217869
this paper describes a non-blind, imperceptible and highly robust hybrid Medical Image Watermarking (MIW) technique for a range of medical data management issues. the method simultaneously addresses medical information security, content authentication, safe archiving and controlled access retrieval. We propose the use of Contourlet Transform (CLT) followed by the Discrete Cosine Transform (DCT) to achieve higher robustness and imperceptibility. Experimental results and performance comparisons confirm the effectiveness and efficiency of the proposed scheme.
In this paper, we present a consumption patternrecognition system based on SVM. It can produce an optimized classification pattern using SVM algorithm and use the pattern to predict consumer behaviors. In this system...
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In modern collaborative filtering applications initial data are typically very large (holding millions of users and items) and come in real time. In this case only incremental algorithms are practically efficient. In ...
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ISBN:
(纸本)9783642217869
In modern collaborative filtering applications initial data are typically very large (holding millions of users and items) and come in real time. In this case only incremental algorithms are practically efficient. In this paper a new algorithm based on the symbiosis of Incremental Singular Value Decomposition (ISVD) and Generalized Hebbian Algorithm (GHA) is proposed. the algorithm does not require to store the initial data matrix and effectively updates user/item profiles when a new user or a new item appears or a matrix cell is modified. the results of experiments show how root mean square error (RMSE) depends on the number of algorithm's iterations and data amount.
A method of approximation the mass of coal moving on a conveyor belt under the ultrasonic sensor that measures a height of coal pile is described in the paper. A process of defining a set of variables that affects the...
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ISBN:
(纸本)9783642217869
A method of approximation the mass of coal moving on a conveyor belt under the ultrasonic sensor that measures a height of coal pile is described in the paper. A process of defining a set of variables that affects the approximated coal mass is presented. A model of multiple regression and an algorithm of regression rules induction based on the M5 algorithm have been exploited to relate momentary values of the coal pile withthe mass of moving coal.
Nowadays, classification is one of the many fields in data Mining, also known as Knowledge Discovery in databases, which aims at extracting information from large data volumes. In order to achieve this, data mining us...
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