Multiview Deep Online Clustering
An Application to Online Research Topic Modeling and Recommendations
Bibliographic Data
| ID | 22108232 |
|---|---|
| Authors | Dipanjyoti Paul (0000-0001-9079-7524, Indian Institute of Technology Patna), Daipayan Chakdar (Indian Institute of Technology Patna), Sriparna Saha (0000-0001-5458-9381, Indian Institute of Technology Patna), Jimson Mathew (0000-0001-8247-9040, Indian Institute of Technology Patna) |
| Year | 2023 |
| Volume | 10 |
| Issue | 5 |
| Pages | 2566-2578 |
| Publication date | 2023-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2022.3187342 |
| OpenAlex | W4289656072 |
| Language | EN |
| Citations received | 1 |
| References cited | 77 |
In today’s scenario, a large number of scientific articles on various domains are being published everyday, resulting in a rapid change in the trends of research topics. Retrieving the trending topics, evaluating the trends and extracting the scope of topics could be beneficial to young researchers, which can be recommended for future scope. Publication of articles is a continuous process, and so is the evolution of topics as well as the scope. The dynamic behavior of topics can be handled by continuously updating the partitioning of incoming articles arriving in a streaming manner. This article proposes an online clustering approach utilizing the autoencoder, which is capable of handling the online stream of articles. The training is performed by considering the multiple views of articles and posing the topic modeling (TM) problem as a multiview (MV) clustering problem. In addition, we employ an evolutionary-based approach to the latent representation of data to automatically determine the number of clusters. To learn the nonlinear mapping appropriately and to generate clusters effectively, the model simultaneously optimizes the reconstruction loss and clustering loss in an MV framework. The developed method has experimented on ArXiv dataset to determine the trending topics, scope, and recommendation of topics. The superiority of the proposed method in generating clusters as well as in determining scope is shown by comparing the results with many existing and baseline methods
Autoencoder · Cluster analysis · Data mining · Data science · Deep learning · Information retrieval · Machine learning · Topic model · Advanced Text Analysis Techniques · Complex Network Analysis Techniques · Computational and Text Analysis Methods · Computer Science · Artificial Intelligence
Parallel Distributed Processing
Visualizing knowledge domains
SciBert
Comparing partitions
Assessing a mixture model for clustering with the integrated completed likelihood
Reducing the Dimensionality of Data with Neural Networks
Normalized cuts and image segmentation
Principal component analysis
An analysis of topic areas and topic trends in theCommunity Mental Health Journal from 1965 through 1977
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,5 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |