To acquire more accurate cT image segmentation results of gallstone, this paper presents a Lore-Based FcM algorithm to conquer the deficiency of tradition FcM method. A penalty term is introduced to objective function...
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ISBN:
(纸本)9781479966004
To acquire more accurate cT image segmentation results of gallstone, this paper presents a Lore-Based FcM algorithm to conquer the deficiency of tradition FcM method. A penalty term is introduced to objective function to enlarge the range of specified class and achieve higher segmentation accuracy. The result of simulation shows that Lore-Based FcM can obviously improve the segmentation quality. The improved algorithm is more rapid and efficiency than the traditional FcM.
This paper proposes an algorithm named Tower Layered FcM to improve the traditional FcM method. A Tower layered structure with a constraint is added into the objective function and pixel's neighbor information is ...
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ISBN:
(纸本)9781479966004
This paper proposes an algorithm named Tower Layered FcM to improve the traditional FcM method. A Tower layered structure with a constraint is added into the objective function and pixel's neighbor information is rational used. The Tower layered structure can speed up the algorithm and constraint the subordinate degree of pixels. So the subordinate degree of clustering center is made more reasonable. The experimental result shows that the new algorithmcan save detail image information and it is more efficient than the traditional FcM.
Data Mining has great scope in the field of medicine. In this article we introduced two new fuzzy approaches for prediction of diabetes disease and liver disorder. Many researchers have proposed the use of K-nearest n...
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ISBN:
(纸本)9781479917341
Data Mining has great scope in the field of medicine. In this article we introduced two new fuzzy approaches for prediction of diabetes disease and liver disorder. Many researchers have proposed the use of K-nearest neighbor (KNN) for diabetes disease prediction. Some have proposed a different approach by using K-meansclustering for preprocessing and then using KNN for classification. In our first approach fuzzyc-meansclustering algorithm is used to cluster the data. Finally, the classification is done using KNN. Similarly, in our second method fuzzyc-meansclustering is followed by classification using fuzzy KNN. PIMA diabetes and liver disorder data sets are used to test our methods. We are able to obtain models more precise than any others available in the literature. The second approach produced better result than the first one. classification is done using ten folds cross-validation technique. The introduction of fuzzyalgorithms certainly has a positive effect on the outcome of diabetes disease and liver disorder prediction models. These fuzzy models led to remarkable increase in classification accuracy.
With regard to rapid development of Internet technology and the increasing volume of data and information, the need for systems that can guide users toward their desired items and services may be felt more than ever. ...
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ISBN:
(纸本)9781479987887
With regard to rapid development of Internet technology and the increasing volume of data and information, the need for systems that can guide users toward their desired items and services may be felt more than ever. As a result, service-relevant data become too big to be effectively processed by traditional approaches. A naive solution is to decrease the number of services that need to be processed in real time. clustering are such techniques that can reduce the data size by a large factor by grouping similar services together. In this research work, we propose a novel approach called fuzzyc-meansclustering-based collaborative Filtering approach (FcM based cF) to overcome the above mentioned issues. Our proposed mechanism consists of two stages: FcM clustering and collaborative filtering. clustering is a preprocessing step to separate the big data into manageable parts. collaborative Filtering is imposed on one of the clusters. The main goal of this research is with respect to users, consolidating and mining their implicit interests from usage reviews or records may be a complement to the explicit interests (ratings). By this means, recommendations can be generated even if there are only few ratings. Finally, our proposed algorithmconsolidates the web services and suggests the better web services based on the user navigation.
Data mining is the process of discovering meaningful new correlation, patterns and trends by sifting through large amounts of data, using pattern recognition technologies as well as statistical and mathematical techni...
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Data mining is the process of discovering meaningful new correlation, patterns and trends by sifting through large amounts of data, using pattern recognition technologies as well as statistical and mathematical techniques. cluster analysis is often used as one of the major data analysis technique widely applied for many practical applications in emerging areas of data mining. Two of the most delegated, partition based clustering algorithms namely k-means and fuzzyc-means are analyzed in this research work. These algorithms are implemented by means of practical approach to analyze its performance, based on their computational time. The telecommunication data is the source data for this analysis. The connection oriented broad band data is used to find the performance of the chosen algorithms. The distance (Euclidian distance) between the server locations and their connections are rearranged after processing the data. The computational complexity (execution time) of each algorithm is analyzed and the results are compared with one another. By comparing the result of this practical approach, it was found that the results obtained are more accurate, easy to understand and above all the time taken to process the data was substantially high in fuzzy c-means algorithm than the k-means. (c) 2014 Elsevier B.V. All rights reserved.
Some of the well-known fuzzyclustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (Gc...
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Some of the well-known fuzzyclustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (Gc) clustering algorithm were developed to detect non-spherical structural clusters. Both, of GG and GK algorithms suffer from the singularity problem of covariance matrix and the effect of initial status. In this paper, a new fuzzy c-means algorithm, based, on Particle Swarm Optimization and Mahalanobis distance without prior information (PSO-FcM-M) is proposed to improve those limitations of GG and GK algorithms. And we point out that the PSO-FcM algorithm is a special case of PSO-FcM-M algorithm. The experimental results of two real data sets show that the performance of our proposed PSO-FcM-M algorithm is better than those of the FcM, GG, GK algorithms.
In order to identify oil pipeline work conditions accurately and quickly, fuzzy c-means algorithm method is applied to this paper. For obtaining clustering standard, sixteen groups of raw data, which include each work...
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ISBN:
(纸本)9781424427994
In order to identify oil pipeline work conditions accurately and quickly, fuzzy c-means algorithm method is applied to this paper. For obtaining clustering standard, sixteen groups of raw data, which include each work condition, are selected from massive pressure data collected in the field. Analyzed data for convenience, each group of raw data is normalized with mean zero and high-frequency noise is eliminated from pressure signal by wavelet transform. The analyzed results on time-domain prove that statistical indexes can clearly and responsively describe pressure variation caused by changed work condition. The paper extracts time-domain statistical indexes from de-noised pressure data as characteristic indexes for fuzzyclustering. comprehensively considered efficiency and accuracy of fuzzy c-means algorithm, six time-domain parameters are regarded as the characteristic indexes. The clustering centers, which are found by fuzzy c-means algorithm with sixteen groups of samples' eigenvectors, are regarded as the standard of pattern recognition for work conditions. It is identified by calculating Euclidean norm between awaiting identification operation status and clustering center. Application results verify that field operation status of oil pipeline is recognized effectively and accurately. The accuracy rate of recognition is by 95%. Especially pipeline leakage is identified accurately.
One of the simple techniques for Data clustering is based on fuzzyc-means (FcM) clustering which describes the belongingness of each data to a cluster by a fuzzy membership function instead of a crisp value. However,...
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One of the simple techniques for Data clustering is based on fuzzyc-means (FcM) clustering which describes the belongingness of each data to a cluster by a fuzzy membership function instead of a crisp value. However, the results of fuzzyclustering depend highly on the initial state selection and there is also a high risk for getting the best results when the datasets are large. In this paper, we present a hybrid algorithm based on FcM and modified stem cells algorithms, we called it Sc-FcM algorithm, for optimum clustering of a dataset into K clusters. The experimental results obtained by using the new algorithm on different well-known datasets compared with those obtained by K-meansalgorithm, FcM, Geneticalgorithm (GA), Particle Swarm Optimization (PSO), Ant colony Optimization (AcO), Artificial Bee colony (ABc) algorithm demonstrate the better performance of the new algorithm. (c) 2013 Elsevier Ltd. All rights reserved.
This paper addresses the automatic blood vessel detection problem in retinal images using matched filtering in an integrated system design platform that involves curvelet transform and fuzzyc-means. Although noise is...
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ISBN:
(纸本)9781479952083
This paper addresses the automatic blood vessel detection problem in retinal images using matched filtering in an integrated system design platform that involves curvelet transform and fuzzyc-means. Although noise is kept constant in medical ccD cameras, due to a number of factors, the contrast between the background and the blood vessels in retinal images and consequently the visual quality of the images looks very poor. Some form of pre-processing operation is therefore essential for the accurate extraction of these blood vessels. Since curvelet transform can represent lines, edges and curvatures very well as compared to other multi-resolution techniques, this paper uses curvelet transform to enhance the retinal vasculature. Matched filtering is then used to intensify the blood vessels which is further employed by fuzzy c-means algorithm to extract the vessel silhouette from the background. Performance is evaluated on publicly available DRIVE database and is compared with the existing blood vessel extraction methodology that uses curvelet transform. Simulation results demonstrate that the proposed method is very much efficient in detecting long and thick as well as short and thin vessels, wherein the existing methods fail to extract tiny and thin vessels.
We propose new features for the language recognition using Gaussian computations. New features are derived from traditional features like Mel frequency cepstral coefficients (MFcc) using fuzzyc-meansclustering algor...
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ISBN:
(纸本)9781479939756
We propose new features for the language recognition using Gaussian computations. New features are derived from traditional features like Mel frequency cepstral coefficients (MFcc) using fuzzyc-meansclustering algorithm. MFcc feature vectors derived from huge corpus of all languages under consideration are grouped into c-clusters using fuzzyc-meansclustering algorithm and one Gaussian distribution is modeled for each cluster. In the training phase, new feature vectors are derived from language specific speech corpus using the clusters which are formed by fuzzyc-meansclustering algorithm. In the testing phase, similar procedure is followed for the extraction of c-element feature vectors from unknown speech utterance, using the same c-Gaussians and evaluated against language specific HMMs. The language apriori knowledge (usefulness of feature vector) has been considered for the improvement of recognition performance. continuous hidden Markov model (cHMM) is designed using the new feature. The languages in OGI database are used for the study and we have achieved good performance.
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