Spatial datamining is a demanding field since huge amounts of spatial data have been collected in various applications, ranging form Remote Sensing to GIS, Computer Cartography, Environmental Assessment and Planning....
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ISBN:
(纸本)3540269231
Spatial datamining is a demanding field since huge amounts of spatial data have been collected in various applications, ranging form Remote Sensing to GIS, Computer Cartography, Environmental Assessment and Planning. Although there have been efforts for spatial association rule mining, but mostly researchers discuss only the positive spatial association rules;they have not considered the spatial negative association rules. Negative association rules are very useful in some spatial problems and are capable of extracting some useful and previously unknown hidden information. We have proposed a novel approach of mining spatial positive and negative association rules. the approach applies multiple level spatial mining methods to extract interesting patterns in spatial and/or non-spatial predicates. data and spatial predicates/association-ship are organized as set hierarchies to mine them level-by-level as required for multilevel spatial positive and negative association rules. A pruning strategy is used in our approach to efficiently reduce the search space. Further efficiency is gained by interestingness measure.
Human motion sequence-oriented spatio-temporal pattern analysis is a new problem in patternrecognition. this paper proposes an approach to human motion sequence recognition based on 2D spatio-temporal shape analysis,...
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ISBN:
(纸本)3540269231
Human motion sequence-oriented spatio-temporal pattern analysis is a new problem in patternrecognition. this paper proposes an approach to human motion sequence recognition based on 2D spatio-temporal shape analysis, which is used to identify diving actions. the approach consists of the following main steps. For each image sequence involving human in diving, a simple exemplar-based contour tracking approach is first used to obtain a 2D contour sequence, which is further converted to an associated temporal sequence of shape features. the shape features are the eigenspace-transformed shape contexts and the curvature information. then, the dissimilarity between two contour sequences is evaluated by fusing (1) the dissimilarity between the associated feature sequences, which is calculated by the Dynamic Time Warping (DTW), and (2) the difference between the pairwise global motion characteristics. Finally, sequence recognition is performed according to a minimum-distance criterion. Experimental results show that high correct recognition ratio can be achieved.
A general automatic method for clinical image segmentation is proposed. Tailored for the clinical environment, the proposed segmentation method consists of two stages: a learning stage and a clinical segmentation stag...
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ISBN:
(纸本)3540269231
A general automatic method for clinical image segmentation is proposed. Tailored for the clinical environment, the proposed segmentation method consists of two stages: a learning stage and a clinical segmentation stage. During the learning stage, manually chosen representative images are segmented using a variational level set method driven by a pathologically modelled energy functional. then a window-based feature extraction is applied to the segmented images. Principal component analysis (PCA) is applied to these extracted features and the results are used to train a support vector machine (SVM) classifier. During the clinical segmentation stage, the input clinical images are classified withthe trained SVM. By the proposed method, we take the strengths of bothmachinelearning and variational level set while limiting their weaknesses to achieve automatic and fast clinical segmentation. Both chest (thoracic) computed tomography (CT) scans (2D and 3D) and dental X-rays are used to test the proposed method. Promising results are demonstrated and analyzed. the proposed method can be used during preprocessing for automatic computer aided diagnosis.
Classification is one of the main tasks in machinelearning, datamining and patternrecognition. Compared withthe extensively studied data-driven approaches, the interactively user-driven approaches are less explore...
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ISBN:
(纸本)0780391365
Classification is one of the main tasks in machinelearning, datamining and patternrecognition. Compared withthe extensively studied data-driven approaches, the interactively user-driven approaches are less explored A granular computing model is suggested for re-examiningthe classification problems. An interactive classification method using the granule network is proposed, which allows multi-strategies for granule tree construction and enhances the understanding and interpretation of the classification process. this method is complementary to the existing classification methods.
Derivative free optimization methods have recently gained a lot of attractions for neural learning. the curse of dimensionality for the neural learning problem makes local optimization methods very attractive;however ...
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ISBN:
(纸本)3540269231
Derivative free optimization methods have recently gained a lot of attractions for neural learning. the curse of dimensionality for the neural learning problem makes local optimization methods very attractive;however the error surface contains many local minima. Discrete gradient method is a special case of derivative free methods based on bundle methods and has the ability to jump over many local minima. there are two types of problems that are associated withthis when local optimization methods are used for neural learning. the first type of problems is initial sensitivity dependence problem - that is commonly solved by using a hybrid model. Our early research has shown that discrete gradient method combining with other global methods such as evolutionary algorithm makes them even more attractive. these types of hybrid models have been studied by other researchers also. Another less mentioned problem is the problem of large weight values for the synaptic connections of the network. Large synaptic weight values often lead to the problem of paralysis and convergence problem especially when a hybrid model is used for fine tuning the learning task. In this paper we study and analyse the effect of different regularization parameters for our objective function to restrict the weight values without compromising the classification accuracy.
Text mining has emerged as a different stream in datamining because of the unstructured nature associated with free text. Many algorithms have been developed to assist in text mining. this paper presents the use of t...
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ISBN:
(纸本)0769524958
Text mining has emerged as a different stream in datamining because of the unstructured nature associated with free text. Many algorithms have been developed to assist in text mining. this paper presents the use of text mining based on a novel high dimensional clustering algorithm that leads to the exploratory datamining on data associated withthe text. Experimental results of analyzing a real-world If text data set and associated data are also presented.
In order to make machinelearning algorithms more usable, our community must be able to design robust systems that offer support to practitioners. In the context of classification, this amounts to developing assistant...
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ISBN:
(纸本)0769524958
In order to make machinelearning algorithms more usable, our community must be able to design robust systems that offer support to practitioners. In the context of classification, this amounts to developing assistants, which deal withthe increasing number of models and techniques, and give advice dynamically on such issues as model selection and method combination. this paper briefly reviews the potential of meta-learning in this context and reports on the early success of a Web-based datamining assistant.
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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this paper discusses a consistency in patterns of language use across domain-specific collections of text. We present a method for the automatic identification of domain-specific keywords - specialist terms - based on...
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Fast control chart patternrecognition aids in instantaneous detection of abnormal functioning of a system. In this paper, we present a parallel algorithm for fast control chart patternrecognition. It addresses three...
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