Data compression is the process of reducing the amount of necessary memory for the representation of a given piece of information. This process is of great utility especially in digital storage and transmission of the...
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ISBN:
(纸本)9781450362382
Data compression is the process of reducing the amount of necessary memory for the representation of a given piece of information. This process is of great utility especially in digital storage and transmission of the multimedia information and it typically involves various encoding/decoding schemes. In this work we will be primarily focused on some compression schemes which employ specific forms of clustering known as fuzzyclustering. In the data mining context, fuzzyclustering is a versatile tool which analyzes heterogeneous collections of data providing insights on the underlying structures involving the concept of partial membership. Several models employing the fuzzyclustering techniques in data compression systems are demonstrated and image compression based on fuzzy transforms for compression and decompression of color videos is described in details.
In this paper, the solutions produced by the fuzzy c-means algorithm for a general class of problems are examined and a method to test for the local optimality of such solutions is established. An equivalent mathemati...
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In this paper, the solutions produced by the fuzzy c-means algorithm for a general class of problems are examined and a method to test for the local optimality of such solutions is established. An equivalent mathematical program is defined for the c-means problem utilizing a generalized norm, then the properties of the resulting optimization problem are investigated. It is shown that the gradient of the resulting objective function at the solution produced by the c-means algorithm in this case takes a special structure which can be used in terminating the algorithm. Moreover, the local optimality of the solution obtained is checked utilizing the Hessian of the criterion function. The solution is a local minimum point if the Hessian matrix at this point is positive semidefinite. Simple rules are proposed to help in checking the definiteness of the matrix.
This paper first reports a data acquisition method that the authors used in a project on modeling driver behavior for microscopic traffic simulations. An advanced instrumented vehicle was employed to collect driver-be...
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This paper first reports a data acquisition method that the authors used in a project on modeling driver behavior for microscopic traffic simulations. An advanced instrumented vehicle was employed to collect driver-behavior data, mainly car-following and lane-changing patterns, on Swedish roads. To eliminate the measurement noise in acquired car-following patterns, the Kalman smoothing algorithm was applied to the state-space model of the physical states (acceleration, speed, and position) of both instrumented and tracked vehicles. The denoised driving patterns were used in the analysis of driver properties in the car-following stage. For further modeling of car-following behavior, we developed and implemented a consolidated fuzzyclustering algorithm to classify different car-following regimes from the preprocessed data. The algorithm considers time continuity of collected driver-behavior patterns and can be more reliably applied in the classification of continuous car-following regimes when the classical fuzzy C-means algorithm gives unclear results.
Cluster analysis is an important exploratory tool which reveals underlying structures in data and organizes them in clusters (groups) based on their similarities. The fuzzy approach to the clustering problem involves ...
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Cluster analysis is an important exploratory tool which reveals underlying structures in data and organizes them in clusters (groups) based on their similarities. The fuzzy approach to the clustering problem involves the concept of partial memberships of the instances in the clusters, increasing the flexibility and enhancing the semantics of the generated clusters. Several fuzzy clustering algorithms have been devised like fuzzy c-means (FCM), Gustafson-Kessel, Gath-Geva, kernel-based FCM etc. Although these algorithms do have a myriad of successful applications, each of them has its stability drawbacks related to several factors including the shape and density of clusters, the presence of noise or outliers and the choices about the algorithm's parameters and cluster center initialization. In this paper we are providing a heterogeneous cluster ensemble approach to improve the stability of fuzzy cluster analysis. The key idea of our methodology is the application of different fuzzy clustering algorithms on the datasets obtaining multiple partitions, which in the later stage will be fused into the final consensus matrix. Finally we have experimentally evaluated and compared the accuracy of this methodology. (C) 2016 The Authors. Published by Elsevier B.V.
This paper first reports a data acquisition method that the authors used in a project on modeling driver behavior for microscopic traffic simulations. An advanced instrumented vehicle was employed to collect driver-be...
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This paper first reports a data acquisition method that the authors used in a project on modeling driver behavior for microscopic traffic simulations. An advanced instrumented vehicle was employed to collect driver-behavior data, mainly car-following and lane-changing patterns, on Swedish roads. To eliminate the measurement noise in acquired car-following patterns, the Kalman smoothing algorithm was applied to the state-space model of the physical states (acceleration, speed, and position) of both instrumented and tracked vehicles. The denoised driving patterns were used in the analysis of driver properties in the car-following stage. For further modeling of car-following behavior, we developed and implemented a consolidated fuzzyclustering algorithm to classify different car-following regimes from the preprocessed data. The algorithm considers time continuity of collected driver-behavior patterns and can be more reliably applied in the classification of continuous car-following regimes when the classical fuzzy C-means algorithm gives unclear results.
Web pages nowadays have different forms and types of content. When the Web content is considered, they are in the form of pictures, videos, audio files, and text files in different languages. The content can be multil...
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ISBN:
(纸本)9788132221265;9788132221258
Web pages nowadays have different forms and types of content. When the Web content is considered, they are in the form of pictures, videos, audio files, and text files in different languages. The content can be multilingual, heterogeneous, and unstructured. The mining should be independent of the language and software. Statistical features of the images are extracted from the pixel map of the image. The extracted features are presented to the fuzzyclustering algorithm (FCM) and Gath-Geva algorithm. The similarity metric being Euclidean distance and Gaussian distance, respectively. The accuracy is compared and presented.
Cluster analysis is an important exploratory tool which reveals underlying structures in data and organizes them in clusters (groups) based on their similarities. The fuzzy approach to the clustering problem involves ...
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Cluster analysis is an important exploratory tool which reveals underlying structures in data and organizes them in clusters (groups) based on their similarities. The fuzzy approach to the clustering problem involves the concept of partial memberships of the instances in the clusters, increasing the flexibility and enhancing the semantics of the generated clusters. Several fuzzy clustering algorithms have been devised like fuzzy c-means (FCM), Gustafson-Kessel, Gath-Geva, kernel-based FCM etc. Although these algorithms do have a myriad of successful applications, each of them has its stability drawbacks related to several factors including the shape and density of clusters, the presence of noise or outliers and the choices about the algorithm's parameters and cluster center initialization. In this paper we are providing a heterogeneous cluster ensemble approach to improve the stability of fuzzy cluster analysis. The key idea of our methodology is the application of different fuzzy clustering algorithms on the datasets obtaining multiple partitions, which in the later stage will be fused into the final consensus matrix. Finally we have experimentally evaluated and compared the accuracy of this methodology.
The management of non point source water pollution in urban areas characteristically involves processes that are difficult to quantify or measure precisely. Two such aspects include the use of publics in plan formulat...
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The management of non point source water pollution in urban areas characteristically involves processes that are difficult to quantify or measure precisely. Two such aspects include the use of publics in plan formulation and monitoring as well as the assessment of the effectiveness (both system and cost effectiveness) of control strategies. We describe the results of our years of studies in which fuzzy set theory and hierarchical modeling provided a useful scientific tool for the evaluation of the effectiveness of these two concerns. fuzzy sets were used in both data generation and analysis. This framework also provided a richer interpretation of our measures of . effectiveness than otherwise possible . The data employed in the studies came principally from urban areas around Georgia and several experimental stations provided by the USGS and EPA in the United States.
Cluster analysis has achieved growing recognition as a useful tool in the analysis of large sets of multivariable chemical data. A new family of clusteringalgorithms is discussed that appear to offer several advantag...
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Cluster analysis has achieved growing recognition as a useful tool in the analysis of large sets of multivariable chemical data. A new family of clusteringalgorithms is discussed that appear to offer several advantages over more traditional approaches. These algorithms are based upon the concept of permitting data samples to possess partial membership in different clusters, thus defining a so-called “fuzzy” partition of the data. By exploiting the fuzzy-set interpretation of the algorithms, researchers can gain valuable insight into the structure of the data.
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