It is well known that fuzzyc-means(FcM) algorithm is one of the most popular methods of cluster ***,the traditional FcM algorithm does not work for the interval-valued data and fuzzy-valued *** this end,a feature map...
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It is well known that fuzzyc-means(FcM) algorithm is one of the most popular methods of cluster ***,the traditional FcM algorithm does not work for the interval-valued data and fuzzy-valued *** this end,a feature mapping method is proposed to preprocess these special type data,and then the traditional FcM algorithmcan also be employed to analyze the interval-valued and fuzzy-valued ***,a novel FcM clustering algorithm is formed for interval-valued data and fuzzy-valued *** experimental result demonstrates its effectiveness.
In GK-algorithm, fuzzyclustering algorithm with preserved volume was used. However, the added fuzzycovariance matrices in their distance measure were not directly derived from the objective function. A fuzzyc-means...
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In GK-algorithm, fuzzyclustering algorithm with preserved volume was used. However, the added fuzzycovariance matrices in their distance measure were not directly derived from the objective function. A fuzzy c-means algorithm based on Mahalanobis distance(FcM-HM) was proposed to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. The singular problem and the selecting initial values problem are improved. We pointed out that the initial memberships of fuzzyc-mean algorithm which was based on Mahalanobis distance algorithm and the traditional fuzzy c-means algorithm(FcM) algorithmcan't be all equal. The other important issue is how to approach the global minimum value that can improve the cluster accuracy. The methods to detect the local extreme value were developed by this paper. Focusing attention to these two faults, an improved new algorithm, "fuzzyc-means based on Particle Swarm Optimization with Mahalanobis distance(PSO-FcM-HM)", is proposed. We have two aims and goals of our research summary. One is to compare the classification accuracies of fuzzyclustering algorithms based on Mahalanobis distances and Euclidean distances. The other is to choose the initial membership to promote the classification accuracies.
There are two problems for clustering algorithm of classicfuzzyc-means (FcM). First, the algoritbm of FcM often obtains different clustering results with the different initial cluster centers because it is over...
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There are two problems for clustering algorithm of classicfuzzyc-means (FcM). First, the algoritbm of FcM often obtains different clustering results with the different initial cluster centers because it is over-dependent on the initial cluster centers. Second, the algorithm needs to know the actual number of clusters in advance, but in fact the number of clusters is unknown. Tbis paper proposes a solution that we determine a reasonable number and centers of clusters using a weighted Euclidean clustering method, and then use the classical FcM algorithm. It can be significantly reduced the number of algorithm iterations. This method was proved feasibility and effectiveness through the emulation experiment.
With the crucial problem of specifying cluster number in clustering algorithm, a cluster number specification-free algorithm, F-cMSVM, is proposed in this paper. Firstly, the data set is classified into two clusters b...
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With the crucial problem of specifying cluster number in clustering algorithm, a cluster number specification-free algorithm, F-cMSVM, is proposed in this paper. Firstly, the data set is classified into two clusters by fuzzy c-means algorithm (FcM). Then the result is tested by Support Vector Machine (SVM) associated with a fuzzy membership function to confirm whether the data set could be classified. Finally, the process is repeated and the clustering result can be obtained. With this unsupervised algorithm, not only does the training data set need no labeling, but also the cluster number needs no specifying. Experiments over networks connection records from KDD cUP 1999 data set were implemented to evaluate the proposed method. To obtain an appropriate training data set and overcome the low efficiency in processing the high dimensional data set, a cross method and a feature selection algorithm based on mutual information were applied respectively in experiments. The result clearly shows the outstanding performance of the proposed method in decision of cluster number and effect of intrusion detection.
This paper presents a formal definition of stable peers, a novel method to separate stable peers from all peers and an analysis of the session sequences of stable peers in P2P (Peer-to-Peer) systems. This study uses t...
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This paper presents a formal definition of stable peers, a novel method to separate stable peers from all peers and an analysis of the session sequences of stable peers in P2P (Peer-to-Peer) systems. This study uses the KAD, a P2P file sharing system with several million simultaneous users, as an example and draws some significant conclusions: (1) large numbers of peers with very short session time usually possess few sessions;(2) the stable peers is about 0.6％ of all peers;(3) the 70％ of stable peers possess very long total session time ensured by a large number of sessions, and possess large difference between session time;(4) the 30％ of stable peers, whose average session time is 1.8 times of the former, possess long total session time, a small number of sessions and high availability. We believe that these two types of stable peers can be used for different functions to solve the churn problem in the hierarchical P2P systems.
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields such as satellite, remote sensing, object identification, face tracking and most importantly medical appli...
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ISBN:
(纸本)9781479980826
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields such as satellite, remote sensing, object identification, face tracking and most importantly medical applications. Here in this paper, we here supposed to propose a novel image segmentation using iterative partitioning mean shift clustering algorithm, which overcomes the drawbacks of conventional clustering algorithms and provides a good segmented images. Simulation performance shows that the proposed scheme has performed superior to the existing clustering methods.
This study proposes an integrated Decision Support System (DSS) with Multi-criteria Decision-Making (McDM) to evaluate trainers in organizations and choose the most suitable one(s) for a training program. The clusteri...
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Urine color is an indicator of health status, especially for patients undergoing medical intervention treatment of urinary catheterization. Urinary tract infections are the most frequently developed infections among p...
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Urine color is an indicator of health status, especially for patients undergoing medical intervention treatment of urinary catheterization. Urinary tract infections are the most frequently developed infections among patients receiving treatment in medical institutions. Such infections can be detected from urine color, like in the case of the purple urine bag syndrome. However, it is a difficult task for non-nursing care star and even the nursing staff to correctly conduct naked-eye identification without proper tools. To better assist both nursing and non-nursing care staff with the detection of infection signs in urine bag patients, a urine color automatic identification device has been developed. The device is based on microcontroller framework and color quantization algorithm. A hybrid color quantization algorithm and two features were proposed to identify the urine color. The identified color, as query data instead of human-described color keyword, can be used to retrieve the information from the database and then find possible symptoms for early warning. Instead of the nursing sta r, the device can automatically identify the patient's urine color. From experimental results, the device with the proposed algorithm shows its capability and feasibility of the urine color automatic identification.
In this paper we study an unsupervised algorithm for radiographic image segmentation, based on the Gaussian mixture models (GMMs). Gaussian mixture models constitute a well-known type of probabilistic neural networks....
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In this paper we study an unsupervised algorithm for radiographic image segmentation, based on the Gaussian mixture models (GMMs). Gaussian mixture models constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation. Mixture model parameters have been trained using the expectation maximization (EM) algorithm. Numerical experiments using radiographic images illustrate the superior performance of EM method in term of segmentation accuracy compared to fuzzy c-means algorithm.
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