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Danh sách kết quả tìm kiếm tài liệu từ 1 đến 6 trong 6 tài liệu phù hợp.
Tài liệu phù hợp với tiêu chí tìm kiếm:
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  • Research on Communication Network Structure Mining Based on Spectrum Monitoring Data

    • Tác giả: LIU, CHANGKUN;  Người hướng dẫn: -;  Người tham gia: WU, XINRONG; ZHU, LEI; YAO, CHANGHUA; YU, LU; WANG, LEI; FAN, HAOREN; PAN, TING (2019)

  • The physical characteristics of the massive spectrum signals carrying the communication information and the statistical laws of these characteristics also potentially reflect the communication behavior of the communication individuals and the intelligence information related to the communication behavior. Intercepting and cracking signal content usually faces enormous difficulties and costs, and more often, we are not able to crack the encrypted signal content. However, by studying the physical features extracted from the spectrum monitoring signals and the statistical laws of these features, it is also possible to dig out the hidden relationships between communication individuals and even the communication network structure, so as to analyze the communication behaviors of the com...
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  • Automatic Concept Extraction Based on Semantic Graphs From Big Data in Smart City

    • Tác giả: Qiu, Jing;  Người hướng dẫn: -;  Người tham gia: Chai, Yuhan; Tian, Zhihong; Du, Xiaojiang; Guizani, Mohsen (2019)

  • With the rapid development of smart cities, various types of sensors can rapidly collect a large amount of data, and it becomes increasingly important to discover effective knowledge and process information from massive amounts of data. Currently, in the field of knowledge engineering, knowledge graphs, especially domain knowledge graphs, play important roles and become the infrastructure of Internet knowledge-driven intelligent applications. Domain concept extraction is critical to the construction of domain knowledge graphs. Although there have been some works that have extracted concepts, semantic information has not been fully used. However, the excellent concept extraction results can be obtained by making full use of semantic information. In this article, a novel concept extr...
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  • Cutting the Unnecessary Long Tail: Cost-Effective Big Data Clustering in the Cloud

    • Tác giả: Li, Dongwei;  Người hướng dẫn: -;  Người tham gia: Wang, Shuliang; Gao, Nan; Yang, Yun (2019)

  • Clustering big data often requires tremendous computational resources where cloud computing is undoubtedly one of the promising solutions. However, the computation cost in the cloud can be unexpectedly high if it cannot be managed properly. The long tail phenomenon has been observed widely in the big data clustering area, which indicates that the majority of time is often consumed in the middle to late stages in the clustering process. In this research, we try to cut the unnecessary long tail in the clustering process to achieve a sufficiently satisfactory accuracy at the lowest possible computation cost. A novel approach is proposed to achieve cost-effective big data clustering in the cloud. By training the regression model with the sampling data, we can make widely used k-means an...
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  • Practical Privacy-Preserving Frequent Itemset Mining on Supermarket Transactions

    • Tác giả: Ma, Chenyang;  Người hướng dẫn: -;  Người tham gia: Wang , Baocang; Jooste, Kyle; Zhang , Zhili; Ping, Yuan (2019)

  • Data mining is widely applied to establish connections among the items in massive datasets nowadays. Association rule mining is one of the most popular methods to perform data mining, and a fundamental part of this is frequent itemset mining. Big-scale data are uploaded to the honest-but-curious cloud service provider (CSP). Therefore, it is imperative to protect the raw data frombeing obtained by the CSP and the third parties. Furthermore, because supermarket transactions are sparse, they are not suitable to be mined using the same methods used for most of the other data. The methods used for ordinary data will cost more computation power if they are applied on this special dataset. In this paper, we propose an efficient protocol to evaluate whether an itemset is frequent or not u...
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  • Group-sparse SVD Models via L1- and L0-norm Penalties and Their Applications in Biological Data

    • Tác giả: Min, Wenwen;  Người hướng dẫn: -;  Người tham gia: Liu, Juan; Zhang, Shihua (2020)

  • Sparse Singular Value Decomposition (SVD) models have been proposed for biclustering high dimensional gene expression data to identify block patterns with similar expressions. However, these models do not take into account prior group effects upon variable selection. To this end, we first propose group-sparse SVD models with group Lasso (GL1-SVD) and group L0-norm penalty (GL0-SVD) for non-overlapping group structure of variables. However, such group-sparse SVD models limit their applicability in some problems with overlapping structure. Thus, we also propose two group-sparse SVD models with overlapping group Lasso (OGL1-SVD) and overlapping group L0-norm penalty (OGL0-SVD). We first adopt an alternating iterative strategy to solve GL1-SVD based on a block coordinate descent method,...
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  • Variable Weighting in Fuzzy k-Means Clustering to Determine the Number of Clusters

    • Tác giả: Khan, Imran;  Người hướng dẫn: -;  Người tham gia: Luo, Zongwei; Huang, Joshua Zhexue; Shahzad, Waseem (2019)

  • One of the most significant problems in cluster analysis is to determine the number of clusters in unlabeled data, which is the input for most clustering algorithms. Some methods have been developed to address this problem. However, little attention has been paid on algorithms that are insensitive to the initialization of cluster centers and utilize variable weights to recover the number of clusters. To fill this gap, we extend the standard fuzzy k-means clustering algorithm. It can automatically determine the number of clusters by iteratively calculating the weights of all variables and the membership value of each object in all clusters. Two new steps are added to the fuzzy k-means clustering process. One of them is to introduce a penalty term to make the clustering process insens...
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