Leveraging Global and Local Topic Popularities for LDA-Based Document Clustering Article Swipe
Peng Yang
,
Yu Yao
,
Huajian Zhou
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.2969525
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.2969525
Document clustering is of high importance for many natural language technologies. A wide range of computational traditional topic models, such as LDA (Latent Dirichlet Allocation) and its variants, have made great progress. However, traditional LDA-based clustering algorithms might not give good results due to such probabilistic models require prior distributions which are always difficult to define. In this paper, we propose a probabilistic model named tpLDA, which incorporates different levels of topic popularity information to determine the prior LDA distribution, discover the latent topics and achieve better clustering. Specifically, global topic popularity is introduced to reduce the potential distraction in local cluster popularity and the local cluster popularity draws more attention on certain parts of the global topic popularity. The two popularities contribute complementary information and their integration can dynamically adjust statistical parameters of the model. Experimental evaluations on real data sets show that, compared with state-of-the-art approaches, our proposed framework dramatically improves the accuracy of documents clustering.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.2969525
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/08970318.pdf
- OA Status
- gold
- Cited By
- 22
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3003736995
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3003736995Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2020.2969525Digital Object Identifier
- Title
-
Leveraging Global and Local Topic Popularities for LDA-Based Document ClusteringWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Peng Yang, Yu Yao, Huajian ZhouList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2020.2969525Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/08970318.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/8948470/08970318.pdfDirect OA link when available
- Concepts
-
Latent Dirichlet allocation, Cluster analysis, Computer science, Popularity, Probabilistic logic, Topic model, Data mining, Artificial intelligence, Machine learning, Psychology, Social psychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
22Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 2, 2023: 2, 2022: 4, 2021: 6Per-year citation counts (last 5 years)
- References (count)
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53Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6679482899, https://openalex.org/W2038043464, https://openalex.org/W2174706414, https://openalex.org/W1974101426, https://openalex.org/W1489223085, https://openalex.org/W2038186431, https://openalex.org/W2030644393, https://openalex.org/W2748596794, https://openalex.org/W2137779536, https://openalex.org/W2887928931, https://openalex.org/W6697606136, https://openalex.org/W2250533720, https://openalex.org/W6683240801, https://openalex.org/W851401968, https://openalex.org/W2197590357, https://openalex.org/W6676481782, https://openalex.org/W2122098402, https://openalex.org/W2059356048, https://openalex.org/W2783064254, https://openalex.org/W2100456378, https://openalex.org/W2035596226, https://openalex.org/W2805308042, https://openalex.org/W81280874, https://openalex.org/W2040422452, https://openalex.org/W2789895350, https://openalex.org/W6636440780, https://openalex.org/W2610222147, https://openalex.org/W2158997610, https://openalex.org/W6684489972, https://openalex.org/W6639619044, https://openalex.org/W2054476043, https://openalex.org/W6674813771, https://openalex.org/W2132827946, https://openalex.org/W2064772995, https://openalex.org/W2800029573, https://openalex.org/W2061873838, https://openalex.org/W6675301171, https://openalex.org/W1987295520, https://openalex.org/W2072644219, https://openalex.org/W1969486090, https://openalex.org/W6734688555, https://openalex.org/W2417560918, https://openalex.org/W2962836999, https://openalex.org/W4231510805, https://openalex.org/W2157006255, https://openalex.org/W2103587173, https://openalex.org/W2100002341, https://openalex.org/W2110798204, https://openalex.org/W2165599843, https://openalex.org/W4293932561, https://openalex.org/W2952478253, https://openalex.org/W1612003148, https://openalex.org/W2130339025 |
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