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Titlebook: Data Mining and Knowledge Discovery for Big Data; Methodologies, Chall Wesley W. Chu Book 2014 Springer-Verlag Berlin Heidelberg 2014 Compu

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樓主: EVOKE
31#
發(fā)表于 2025-3-26 22:20:37 | 只看該作者
A Clustering Approach to Constrained Binary Matrix Factorization, binary matrix is minimal. BMF has served as an important tool in dimension reduction for high-dimensional data sets with binary attributes and has been successfully employed in numerous applications. In the existing literature on BMF, the matrix product is not required to be binary. We call this .
32#
發(fā)表于 2025-3-27 01:31:31 | 只看該作者
33#
發(fā)表于 2025-3-27 06:41:47 | 只看該作者
Book 2014 of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction
34#
發(fā)表于 2025-3-27 10:10:23 | 只看該作者
35#
發(fā)表于 2025-3-27 16:28:11 | 只看該作者
36#
發(fā)表于 2025-3-27 19:27:57 | 只看該作者
37#
發(fā)表于 2025-3-27 22:02:03 | 只看該作者
38#
發(fā)表于 2025-3-28 06:10:33 | 只看該作者
InfoSearch: A Social Search Engine,he question, within the boundary of only one hop in a social network topology, how can we rank the results shared by friends. We develop . over the Facebook platform to leverage information shared by users in Facebook. We provide a comprehensive study of factors that may have a potential impact on s
39#
發(fā)表于 2025-3-28 09:57:30 | 只看該作者
A Generalized Approach for Social Network Integration and Analysis with Privacy Preservation,cuss aspects of sharing the insensitive and generalized information of social networks to support social network analysis while preserving the privacy at the same time. We discuss the generalization approach to construct a generalized social network in which only insensitive and generalized informat
40#
發(fā)表于 2025-3-28 11:35:39 | 只看該作者
A Clustering Approach to Constrained Binary Matrix Factorization,tionship between the BLP subproblem and clustering to develop an effective 2- approximation algorithm for CBMF when the underlying matrix has very low rank. The proposed algorithm can also provide a 2-approximation to rank-1 UBMF. We also develop a randomized algorithm for CBMF and estimate the appr
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