Zobrazeno 1 - 10
of 6 536
pro vyhledávání: '"Data stream clustering"'
Publikováno v:
Mathematics, Vol 12, Iss 13, p 2049 (2024)
Clustering data streams has become a hot topic and has been extensively applied to many real-world applications. Compared with traditional clustering, data stream clustering is more challenging. Adaptive Resonance Theory (ART) is a powerful (online)
Externí odkaz:
https://doaj.org/article/7096606e7c9b4cb4852dfe6c7ead1b60
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 15, Iss 1, Pp 1-18 (2022)
Abstract Identifying clusters of arbitrary shapes and constantly processing the newly arrived data points are two critical challenges in the study of clustering. This paper proposes a dynamic weight and density peaks clustering algorithm to simultane
Externí odkaz:
https://doaj.org/article/be4ab7b52a414ecea61f02e84e04ae67
Publikováno v:
Leida xuebao, Vol 11, Iss 3, Pp 418-433 (2022)
Radar emitter signal deinterleaving is a key technology for radar signal reconnaissance and an essential part of battlefield situational awareness. This paper systematically sorts out the mainstream technology of radar emitter signal deinterleaving.
Externí odkaz:
https://doaj.org/article/85f8152211224f888066138940e23c20
Publikováno v:
IEEE Access, Vol 10, Pp 579-596 (2022)
Various applications, such as electronic business, satellite remote sensing, intrusion discovery, and network traffic monitoring, generate large unbounded data stream sequences at a rapid pace. The clustering of data streams has attracted considerabl
Externí odkaz:
https://doaj.org/article/8de2244d65d14a5cabde1c667c334bc2
Publikováno v:
المجلة العراقية للعلوم الاحصائية, Vol 18, Iss 1, Pp 72-88 (2021)
Clustering data streams is one of the prominent tasks of discovering hidden patterns in data streams. It refers to the process of clustering newly arrived data into continuously and dynamically changing segmentation patterns. The current data stream
Externí odkaz:
https://doaj.org/article/e289e85bd19a4fc69ce5bbc10de3b981
Autor:
Ammar Abd Alazeez
Publikováno v:
Al-Rafidain Journal of Computer Sciences and Mathematics, Vol 14, Iss 1, Pp 67-82 (2020)
Data stream clustering refers to the process of grouping continuously arriving new data chunks into continuously changing groups to enable dynamic analysis of segmentation patterns. However, the main attention of research on clustering methods till n
Externí odkaz:
https://doaj.org/article/cf64f36cc65341d98ab53705ce97a79d
Publikováno v:
Frontiers in Bioengineering and Biotechnology, Vol 9 (2022)
Foot contact detection is critical for legged robot running control using state machine, in which the controller uses different control modules in the leg flight phase and landing phase. This paper presents an online learning framework to improve the
Externí odkaz:
https://doaj.org/article/9381529b82cb405d877aac02393e3169
Publikováno v:
Vietnam Journal of Computer Science, Vol 6, Iss 2, Pp 223-256 (2019)
The spread of real-time applications has led to a huge amount of data shared between users. This vast volume of data rapidly evolving over time is referred to as data stream. Clustering and processing such data poses many challenges to the data minin
Externí odkaz:
https://doaj.org/article/4507300c24e445f29e11a756e627763d
Autor:
Conor Fahy, Shengxiang Yang
Publikováno v:
IEEE Access, Vol 7, Pp 127128-127140 (2019)
Change in a data stream can occur at the concept level and at the feature level. Change at the feature level can occur if new, additional features appear in the stream or if the importance and relevance of a feature changes as the stream progresses.
Externí odkaz:
https://doaj.org/article/e13cc7c3d1cb40be9754a4285f3e3e47
Publikováno v:
IEEE Access, Vol 6, Pp 51269-51285 (2018)
An accurate scenario of customer's power consumption patterns is a worthwhile asset for electricity provider. This paper proposes a cluster survival model of concept drift in load profile data. The cluster survival model of concept drift retrieves th
Externí odkaz:
https://doaj.org/article/572d5ab564dc42a991c286b5c0817ae6