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Wang Y.,Guangdong University of Technology | Wang Y.,South China University of Technology | Li Y.,Guangdong University of Technology | Li Y.,Shenzhen Key Laboratory of High Performance Data Mining | And 3 more authors.
International Journal of Distributed Sensor Networks | Year: 2013

In order to improve the sensing accuracy of the Cognitive Radio Sensor Networks and reduce the interference to the primary user, this paper proposes an improved optimal linear weighted cooperative spectrum sensing scheme on the assumption that the report channel is not ideal. Through mathematical modeling, the spectrum sensing problem is ultimately converted into a constrained nonconvex optimization problem, and the chaotic harmony search (CHS) algorithm is to be used to find the optimal weighting vector value. The simulation results show that the proposed linear cooperative spectrum detection scheme based on the CHS algorithm has better performance than HS, SFLA, EGC, MRC, and MDC algorithm. In addition, the influence of local noise power, report channel noise power, and report channel gain on the performance of the algorithm is analyzed by simulation. The results show that local noise power has greater impact on the sensing performance. © 2013 Yonghua Wang et al. Source


He B.,Chongqing University of Technology | He B.,Shenzhen Key Laboratory of High Performance Data Mining
Proceedings of the 2nd International Conference on Electronic and Mechanical Engineering and Information Technology, EMEIT 2012 | Year: 2012

There were some problems in traditional mining algorithm of association rules: a lot of candidate itemsets and communication traffic. Aiming at these problems, this paper proposed a fast mining algorithm of association rules based on cloud computing, namely, FMAAR algorithm. Firstly, the frequent items were found. Secondly, the FP-tree was created and the frequent itemsets were mined by FP-growth algorithm. Finally, the association rules were got by cloud computing. The experimental results suggest that FMAAR algorithm is fast and effective. © the authors. Source


He B.,Chongqing University of Technology | He B.,Nanjing University | He B.,Shenzhen Key Laboratory of High Performance Data Mining
Lecture Notes in Electrical Engineering | Year: 2016

The paper proposed the algorithm for mining global frequent itemsets based on cloud computing, namely MGFICC algorithm. MGFICC algorithm made each nodes compute local frequent itemsets by FP-growth algorithm and mapreduce, then the center node exchanged data with other nodes, finally, global frequent itemsets were gained by mapreduce. MGFICC algorithm required less communication traffic by the searching strategies of top-down and bottom-up. Theoretical analysis and experimental results suggest that MGFICC algorithm is fast. © Springer India 2016. Source


Xiong T.,CAS Shenzhen Institutes of Advanced Technology | Xiong T.,Universite de Sherbrooke | Wang S.,Universite de Sherbrooke | Jiang Q.,CAS Shenzhen Institutes of Advanced Technology | And 2 more authors.
IEEE Transactions on Knowledge and Data Engineering | Year: 2014

Clustering categorical sequences is an important and difficult data mining task. Despite recent efforts, the challenge remains, due to the lack of an inherently meaningful measure of pairwise similarity. In this paper, we propose a novel variable-order Markov framework, named weighted conditional probability distribution (WCPD), to model clusters of categorical sequences. We propose an efficient and effective approach to solve the challenging problem of model initialization. To initialize the WCPD model, we propose to use a first-order Markov model built on a weighted fuzzy indicator vector representation of categorical sequences, which we call the WFI Markov model. Based on a cascade optimization framework that combines the WCPD and WFI models, we design a new divisive hierarchical clustering algorithm for clustering categorical sequences. Experimental results on data sets from three different domains demonstrate the promising performance of our models and clustering algorithm. © 1989-2012 IEEE. Source


Chen J.,Guangdong University of Technology | Chen J.,Sun Yat Sen University | Chen J.,Shenzhen Key Laboratory of High Performance Data Mining | Ma Z.,Sun Yat Sen University | Liu Y.,Sun Yat Sen University
IEEE Transactions on Neural Networks and Learning Systems | Year: 2013

Dimensionality reduction is vital in many fields, and alignment-based methods for nonlinear dimensionality reduction have become popular recently because they can map the high-dimensional data into a low-dimensional subspace with the property of local isometry. However, the relationships between patches in original high-dimensional space cannot be ensured to be fully preserved during the alignment process. In this paper, we propose a novel method for nonlinear dimensionality reduction called local coordinates alignment with global preservation. We first introduce a reasonable definition of topology-preserving landmarks (TPLs), which not only contribute to preserving the global structure of datasets and constructing a collection of overlapping linear patches, but they also ensure that the right landmark is allocated to the new test point. Then, an existing method for dimensionality reduction that has good performance in preserving the global structure is used to derive the low-dimensional coordinates of TPLs. Local coordinates of each patch are derived using tangent space of the manifold at the corresponding landmark, and then these local coordinates are aligned into a global coordinate space with the set of landmarks in low-dimensional space as reference points. The proposed alignment method, called landmarks-based alignment, can produce a closed-form solution without any constraints, while most previous alignment-based methods impose the unit covariance constraint, which will result in the deficiency of global metrics and undesired rescaling of the manifold. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed algorithm. © 2012 IEEE. Source

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