Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Hangyuan DU"'
Publikováno v:
大数据, Vol 10, Pp 106-120 (2024)
In semi-supervised network representation learning, node labels play an important role in guiding the establishment of network mapping relationships among different spaces.However, in many practical tasks, the available label information is usually l
Externí odkaz:
https://doaj.org/article/df2f366cf4a44641813b0b45d9c2df7d
Publikováno v:
Information Sciences. 630:222-234
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b875759f22c30e622621118f5ee7f06b
https://doi.org/10.1016/j.ins.2023.02.054
https://doi.org/10.1016/j.ins.2023.02.054
Publikováno v:
Neurocomputing. 535:144-155
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::242b3238ff8e241c57104e2069259d76
https://doi.org/10.1016/j.neucom.2023.03.016
https://doi.org/10.1016/j.neucom.2023.03.016
Publikováno v:
Wireless Communications and Mobile Computing. 2022:1-17
In recent years, Internet of Things has not only promoted the continuous development of e-commerce transaction but also brought loop-hole to the fraud gangs who always utilize mobile devices to commit fraud crimes. For example, fraud gangs are usuall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3cf22ad50ed55b06b9d994c6070be96d
https://doi.org/10.1155/2022/3766810
https://doi.org/10.1155/2022/3766810
Publikováno v:
Pattern Recognition. 142:109644
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e64dc90d5a1dcb9ec406201fe0f303bb
https://doi.org/10.1016/j.patcog.2023.109644
https://doi.org/10.1016/j.patcog.2023.109644
Publikováno v:
Wireless Communications and Mobile Computing.
Data analysis is the foundation of Internet of Things (IoT) based applications, and clustering is an effective technology of data analysis. Clustering ensemble integrates multiple base clustering results to obtain a consensus result and thus improves
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6fad708bc25bb9bd6a9b9bfe53601911
Publikováno v:
Knowledge-Based Systems. 143:58-64
Efficiently discovering the hidden community structure in a network is an important research concept for graph clustering. Although many detection algorithms have been proposed, few of them provide a visual understanding of the community structure in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4aedb1354af319f61394b0d85873dd79
https://doi.org/10.1016/j.knosys.2017.12.007
https://doi.org/10.1016/j.knosys.2017.12.007
Publikováno v:
SCIENTIA SINICA Informationis. 47:1464-1482
An optimization model based on the fuzzy maximum entropy method is proposed for the data stream evolving clustering problem. In the model, the fuzziness and effectiveness of cluster partition are described by fuzzy membership and information entropy,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cba88560ecea3cdff3edd43da17df4b7
https://doi.org/10.1360/n112017-00107
https://doi.org/10.1360/n112017-00107
Publikováno v:
Pattern Recognition. 106:107411
The label propagation algorithm is a well-known semi-supervised clustering method, which uses pre-given partial labels as constraints to predict the labels of unlabeled data. However, the algorithm has the following limitations: (1) it does not fully
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1134d705de9059d44d792746c5c220be
https://doi.org/10.1016/j.patcog.2020.107411
https://doi.org/10.1016/j.patcog.2020.107411
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-1
Cluster ensemble is a very important tool that aggregates several base clusterings to generate a single output clustering with improved robustness and stability. However, the quality of the final clustering is often affected by uncertainties on the g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f9ef459020f99c1f6fe636444bb117b
https://doi.org/10.1109/tkde.2018.2865954
https://doi.org/10.1109/tkde.2018.2865954