Modulation Recognition of MQAM Signals Based on Semi Supervised Clustering Theory

2014 ◽  
Vol 519-520 ◽  
pp. 975-978
Author(s):  
Ping Ping Li ◽  
Gang Can Sun ◽  
Jin Yuan Shen
Keyword(s):  
Clustering Algorithm ◽  
Error Function ◽  
Computation Complexity ◽  
Modulation Recognition ◽  
Characteristic Parameters ◽  
Recognition Method ◽  
Supervised Clustering ◽  
Clustering Center ◽  
Different Order ◽  
Cluster Points

In the modulation recognition of MQAM signals cluster points of traditional clustering algorithm were not accurate, iterations of the algorithm are multiple and the curve of square error function was not smooth. To solve these problems, this paper presents a theory of modulation recognition method for reconstruction of MQAM signal constellation diagram based on semi supervised clustering, using labeled samples to guide the membership degree and the clustering center update. Analysis the receiving constellation and extracting the characteristic parameters R of constellation compared with parameter Rs of standard constellation, to realize modulation recognition of the different order of MQAM signal. The results show that the method to identify the MQAM signal at rate of 90. Convergence of iterative process is reduced from 40 to 8 times. The algorithm has low computation complexity and the square error function curve is smooth.

Semi-Supervised Clustering for the Identification of Different Cancer Types Using the Gene Expression Profiles

2012 ◽  
pp. 50-66
Author(s):  
Manuel Martín-Merino
Keyword(s):  
Gene Expression ◽  
Linear Combination ◽  
Clustering Algorithm ◽  
Expression Profiles ◽  
Error Function ◽  
Clustering Algorithms ◽  
Gene Expression Profiles ◽  
Data Sets ◽  
Supervised Clustering ◽  
Supervised Learning Algorithms

DNA Microarrays allow for monitoring the expression level of thousands of genes simultaneously across a collection of related samples. Supervised learning algorithms such as -NN or SVM (Support Vector Machines) have been applied to the classification of cancer samples with encouraging results. However, the classification algorithms are not able to discover new subtypes of diseases considering the gene expression profiles. In this chapter, the author reviews several supervised clustering algorithms suitable to discover new subtypes of cancer. Next, he introduces a semi-supervised clustering algorithm that learns a linear combination of dissimilarities from the a priory knowledge provided by human experts. A priori knowledge is formulated in the form of equivalence constraints. The minimization of the error function is based on a quadratic optimization algorithm. A norm regularizer is included that penalizes the complexity of the family of distances and avoids overfitting. The method proposed has been applied to several benchmark data sets and to human complex cancer problems using the gene expression profiles. The experimental results suggest that considering a linear combination of heterogeneous dissimilarities helps to improve both classification and clustering algorithms based on a single similarity.

Semi-Supervised Clustering for the Identification of Different Cancer Types Using the Gene Expression Profiles

2013 ◽  
pp. 1609-1625
Author(s):  
Manuel Martín-Merino
Keyword(s):  
Gene Expression ◽  
Linear Combination ◽  
Clustering Algorithm ◽  
Expression Profiles ◽  
Error Function ◽  
Clustering Algorithms ◽  
Gene Expression Profiles ◽  
Data Sets ◽  
Supervised Clustering ◽  
Supervised Learning Algorithms

DNA Microarrays allow for monitoring the expression level of thousands of genes simultaneously across a collection of related samples. Supervised learning algorithms such as k-NN or SVM (Support Vector Machines) have been applied to the classification of cancer samples with encouraging results. However, the classification algorithms are not able to discover new subtypes of diseases considering the gene expression profiles. In this chapter, the author reviews several supervised clustering algorithms suitable to discover new subtypes of cancer. Next, he introduces a semi-supervised clustering algorithm that learns a linear combination of dissimilarities from the a priory knowledge provided by human experts. A priori knowledge is formulated in the form of equivalence constraints. The minimization of the error function is based on a quadratic optimization algorithm. A L2 norm regularizer is included that penalizes the complexity of the family of distances and avoids overfitting. The method proposed has been applied to several benchmark data sets and to human complex cancer problems using the gene expression profiles. The experimental results suggest that considering a linear combination of heterogeneous dissimilarities helps to improve both classification and clustering algorithms based on a single similarity.

Improved K-Means Clustering Method Based on Complex Network for Rolling Bearing Fault Diagnosis

2013 ◽  
Vol 273 ◽  
pp. 250-254
Author(s):  
Yang Pan ◽  
An Hua Chen ◽  
Ling Li Jiang
Keyword(s):  
Fault Diagnosis ◽  
Complex Network ◽  
Clustering Algorithm ◽  
Rolling Bearing ◽  
Node Degree ◽  
Recognition Method ◽  
Bearing Fault Diagnosis ◽  
Initial Cluster ◽  
Clustering Center ◽  
Selection Of

According to the selection difficulties of initial clustering center of k-means clustering algorithm, this paper proposes a method that is to use complex network degree to improve k-means clustering algorithm for fault pattern recognition method, and to improve the accuracy of clustering. Use network to represent fault data structure, with joint connecting matrix to express similarity between nodes, according to the complex concepts of networks degree, calculate the size of every node degree, and select the maximum degree of node as k-means clustering initial center. This method is applied to the rolling bearing clustering diagnosis example, achieving good fault diagnosis effect. This study provides a new method for the selection of initial cluster centers of K-means clustering.

Segmentation of corpus callosum based on tensor fuzzy clustering algorithm

2021 ◽  
pp. 1-14
Author(s):  
Yujia Qu ◽  
Yuanjun Wang
Keyword(s):  
Corpus Callosum ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Midsagittal Plane ◽  
Fuzzy Clustering Algorithm ◽  
Human Connectome Project ◽  
Clustering Center ◽  
Fixed Region

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

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