caa a22 4500 605468591 CHVBK 20210128100317.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s00500-014-1343-4 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1343-4 Double-phase locality-sensitive hashing of neighborhood development for multi-relational data [Elektronische Daten] [Ping Ling, Xiangsheng Rong, Yongquan Dong, Guosheng Hao] Multi-relational (MR) data refer to the objects that involve multi-associated tables or a relational database, and they are widely used in diverse applications. In spite of rich achievements in concrete classification or regression tasks, a neighborhood development algorithm customized to MR data is still missing. The reason lies in the fact that MR data are high-dimensional and highly structured. To address these two difficulties, this paper presents a double-phase locality-sensitive hashing (DPLSH) algorithm to develop neighborhood for MR data. DPLSH consists of offline and online hashing schemas to draw and summarize local closeness information from each involved table. Based on the closeness information, three criteria of neighborhood development are proposed. DPLSH is encoded with parameterization heuristics to make the algorithm data adaptive and less costly. Extensive experiments indicate that for MR data, the quality of neighborhoods produced by DPLSH is better than its peers; besides, in common data environment, DPLSH also exhibits the competitive behaviors with the state of the art. Springer-Verlag Berlin Heidelberg, 2014 Multi-relational data nationallicence Neighborhood development nationallicence Locality-sensitive hashing nationallicence Double-phase approach nationallicence Parameterization nationallicence Ling Ping College of Computer Science and Technology, Jiangsu Normal University, 221116, Xuzhou, China aut Rong Xiangsheng Department of Training, Air Force Logistics of P. L. A, 221000, Xuzhou, China aut Dong Yongquan College of Computer Science and Technology, Jiangsu Normal University, 221116, Xuzhou, China aut Hao Guosheng College of Computer Science and Technology, Jiangsu Normal University, 221116, Xuzhou, China aut Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1553-1565 1432-7643 19:6<1553 2015 19 500 https://doi.org/10.1007/s00500-014-1343-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1343-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ling Ping College of Computer Science and Technology, Jiangsu Normal University, 221116, Xuzhou, China aut NATIONALLICENCE 700 1- Rong Xiangsheng Department of Training, Air Force Logistics of P. L. A, 221000, Xuzhou, China aut NATIONALLICENCE 700 1- Dong Yongquan College of Computer Science and Technology, Jiangsu Normal University, 221116, Xuzhou, China aut NATIONALLICENCE 700 1- Hao Guosheng College of Computer Science and Technology, Jiangsu Normal University, 221116, Xuzhou, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1553-1565 1432-7643 19:6<1553 2015 19 500