There are lots of algorithms for optimal clustering. The main part of clustering algorithms includes the K-means, fuzzyc-means (FcM) and evolution algorithm. The main purpose of this paper was to research the perform...
详细信息
There are lots of algorithms for optimal clustering. The main part of clustering algorithms includes the K-means, fuzzyc-means (FcM) and evolution algorithm. The main purpose of this paper was to research the performance and characteristics of these three types of algorithms. One criteria (clustering validity index), namely TRW, was used in the optimisation of classification and eight real-world datasets (glass, wine, ionosphere, biodegradation, connectionist bench, hill-valley, musk, madelon datasets), whose dimension became higher, were applied. We made a performance analysis and concluded that it was easy of the K-means and FcM to fall into a local minimum, and the hybrid algorithm was found more reliable and more efficient, especially on difficult tasks with high dimension.
Objectives: To find discriminative combination of influential factors of Intracerebral hematoma (IcH) to cluster IcH patients with similar features to explore relationship among influential factors and 30-day mortalit...
详细信息
Objectives: To find discriminative combination of influential factors of Intracerebral hematoma (IcH) to cluster IcH patients with similar features to explore relationship among influential factors and 30-day mortality of IcH. Methods: The data of IcH patients are collected. We use a decision tree to find discriminative combination of the influential factors. We cluster IcH patients with similar features using fuzzy c-means algorithm (FcM) to construct a support vector machine (SVM) for each cluster to build a multi-SVM classifier. Finally, we designate each testing data into its appropriate cluster and apply the corresponding SVM classifier of the cluster to explore the relationship among impact factors and 30-day mortality. Results:The two influential factors chosen to split the decision tree are Glasgow coma scale (GcS) score and Hematoma size. FcM algorithm finds three centroids, one for high danger group, one for middle danger group, and the other for low danger group. The proposed approach outperforms benchmark experiments without FcM algorithm to cluster training data. conclusions: It is appropriate to construct a classifier for each cluster with similar features. The combination of factors with significant discrimination as input variables should outperform that with only single discriminative factor as input variable.
Some periodic and quasi-periodic pulse trains are emitted by different sources in the environment and a number of sensors receive them through a single channel simultaneously. We are often interested in separating the...
详细信息
ISBN:
(纸本)9781509008889
Some periodic and quasi-periodic pulse trains are emitted by different sources in the environment and a number of sensors receive them through a single channel simultaneously. We are often interested in separating these pulse trains for source identification at sensors. This identification process is termed as deinterleaving pulse trains. Deinterleaving pulse trains has wide applications in communications, radar systems, neural systems, biomedical engineering, and so on. This paper studies the deinterleaving problem with the assumption that both sources and sensors are fixed. In this study, the problem of deinterleaving pulse trains is modeled as a blind source separation (BSS) problem. To solve the BSS problem, we propose a novel geometry-based producer that has not been discussed in the literature yet. The proposed method has superiority over the previous ones in a number of aspects. First, it is a computationally simple method. Second, the proposed algorithm is capable of deinterleaving similar pulse trains. Third, it is able to separate pulse trains with complex pulse repetition interval (PRI) modulations. Finally, the algorithm's performance is not influenced by missing and spurious pulses. Some numerical simulations are provided in order to illustrate the effectiveness of the proposed method.
Suppressed fuzzyc-means (s-FcM) clustering was introduced with the intention of combining the higher convergence speed of hard c-means (HcM) clustering with the finer partition quality of fuzzyc-means (FcM) algorith...
详细信息
ISBN:
(纸本)9783642162916
Suppressed fuzzyc-means (s-FcM) clustering was introduced with the intention of combining the higher convergence speed of hard c-means (HcM) clustering with the finer partition quality of fuzzyc-means (FcM) algorithm. Suppression modifies the FcM iteration by creating a competition among clusters: lower degrees of memberships are reduced via multiplication with a previously set constant suppression rate, while the largest fuzzy membership grows by swallowing all the suppressed parts of the small ones. Suppressing the FcM algorithm was found successful in terms of accuracy and working time. In this paper we introduce some generalized formulations of the suppression rule, leading to an infinite number of new clustering algorithms. Based on a large amount of numerical tests performed in multidimensional environment, some generalized forms of suppression proved to give more accurate partitions than FcM and s-FcM.
Suppressed fuzzyc-means (s-FcM) clustering was introduced in Fan et al. (Pattern Recogn Lett 24: 1607-1612, 2003) with the intention of combining the higher speed of hard c-means (HcM) clustering with the better clas...
详细信息
ISBN:
(纸本)3540882685
Suppressed fuzzyc-means (s-FcM) clustering was introduced in Fan et al. (Pattern Recogn Lett 24: 1607-1612, 2003) with the intention of combining the higher speed of hard c-means (HcM) clustering with the better classification properties of fuzzyc-means (FcM) algorithm. The authors modified the FcM iteration to create a competition among clusters: lower degrees of memberships were diminished according to a previously set suppression rate, while the largest fuzzy membership grew by swallowing all the suppressed parts of the small ones. Suppressing the FcM algorithm was found successful in the terms of accuracy and working time, but the authors failed to answer a series of important questions. In this paper, we clarify the view upon the optimality and the competitive behavior of s-FcM via analytical computations and numerical analysis. A quasi competitive learning rate (QLR) is introduced first, in order to quantify the effect of suppression. As the investigation of s-FcM's optimality did not provide a precise result, an alternative, optimally suppressed FcM (Os-FcM) algorithm is proposed as a hybridization of FcM and HcM. Both the suppressed and optimally suppressed FcM algorithms underwent the same analytical and numerical evaluations, their properties were analyzed using the QLR. We found the newly introduced Os-FcM algorithm quicker than s-FcM at any nontrivial suppression level. Os-FcM should also be favored because of its guaranteed optimality.
In this paper, an effective method based on the smallest repeat unit recognition (SRUR) algorithm is proposed to inspect the color effect of yarn-dyed fabric automatically. This method consists of three main steps: (1...
详细信息
In this paper, an effective method based on the smallest repeat unit recognition (SRUR) algorithm is proposed to inspect the color effect of yarn-dyed fabric automatically. This method consists of three main steps: (1) color pattern preliminary recognition;(2) weave repeat unit recognition;(3) color yarn repeat unit recognition. In the first step, the floats in the fabric are located by yarn position segmented with mathematical statistics of sub-images and the colors of all floats classified by the fuzzy c-means algorithm. The color yarn layout is recognized by statistical analysis and the color pattern is roughly generated. In the second step, the weave repeat unit is found based on the preliminary color pattern. The weave repeat unit is extracted from the incompletely recognized weave pattern matrix by the SRUR algorithm. In the last step, according to the weave repeat unit and the preliminary identified color pattern, the color yarn layout is rectified by the improved statistical analysis, and the color yarn repeat unit is finally obtained by the SRUR algorithm. According to the weave and color yarn repeat units, the color effect is produced. The experimental analysis proved that the proposed method can recognize color effects of yarn-dyed fabrics with satisfactory accuracy.
To extract complete hand gesture region under complex dynamic background and to effectively solve problems of skin-color interference and varying illumination, we present a novel dynamic hand gesture segmentation meth...
详细信息
To extract complete hand gesture region under complex dynamic background and to effectively solve problems of skin-color interference and varying illumination, we present a novel dynamic hand gesture segmentation method which combines unequal probabilities and improved fuzzyc-means (FcM) algorithm. Firstly, this method utilizes unequal-probabilities to build background model of complex dynamic background and detects motion region of hand gesture with background difference. Secondly, FcM algorithm is improved to accelerate the rate of convergence, cluster hand gesture image and distinguish skin-color region and non-skin-color region. Finally, we process motion region and skin-color region with logic operation and morphological operation, and complete dynamic gesture segmentation under complex background. The experimental results illustrate that the proposed method has high accuracy and is robust to skin-color interference and varying illumination.
Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, in...
详细信息
Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes' status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a fuzzy c-means algorithm (FcMA) and Sorting and classification algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability.
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 (GG...
详细信息
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 (GG) clustering algorithm were developed to detect non-spherical structural clusters. However, GK algorithm needs added constraint of fuzzycovariance matrix, GK algorithmcan only be used for the data with multivariate Gaussian distribution. A fuzzy c-means algorithm based on Mahalanobis distance (FcM-M) was proposed by our previous work to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. In this paper, A improved fuzzy c-means algorithm based on a common Mahalanobis distance (FcM-cM) is proposed The experimental results of three real data sets show that the performance of our proposed FcM-cM algorithm is better than those of the FcM, GG, GK and FcM-M algorithms.
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...
详细信息
ISBN:
(纸本)9781479980819
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.
暂无评论