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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 14/2024

02.03.2024 | Original Article

A topic-enhanced dirichlet model for short text stream clustering

verfasst von: Kan Liu, Jiarui He, Yu Chen

Erschienen in: Neural Computing and Applications | Ausgabe 14/2024

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Abstract

Short text streams, such as social media comments, are continuously generated, making effective clustering methods essential for extracting valuable information. However, existing research fails to address the problem of topic concentration in clustering, which leads to multiple topics being confused in one cluster, making it challenging to summarize the center of clustering. To tackle this issue, this paper proposes a novel topic-enhanced clustering method called TEDM, based on the Dirichlet model. The method uses dynamic clustering, leveraging topic information to improve the sampling of documents and better cluster documents on the same topic. TEDM constructs a dynamic word relation graph to extract topic terms, which is updated with the stream of documents to cope with the dynamic changes in topics. Extensive experimental studies demonstrate that TEDM outperforms state-of-the-art works on multiple real datasets.
Vorheriger Artikel On the adversarial robustness of generative autoencoders in the latent space
Nächster Artikel Dual-stage classification for lung cancer detection and staging using hybrid deep learning techniques

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Fußnoten
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Metadaten
Titel
A topic-enhanced dirichlet model for short text stream clustering
verfasst von
Kan Liu
Jiarui He
Yu Chen
Publikationsdatum
02.03.2024
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 14/2024
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-024-09480-w

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