Name
Abstract | ||
|---|---|---|
Latent Dirichlet Allocation (LDA) has been widely applied to text mining. LDA is a probabilistic topic model which processes documents as the probability distribution of topics. One challenging issue in application of LDA is to select the optimal number of topics in LDA model. This paper presents a topic selection method which considers the density of each topic and computes the most unstable topic structure through an iteration process. Evaluation results show that the proposed method can generate an optimal number of topics automatically with a small number of iterations. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/FSKD.2014.6980931 | FSKD |
Keywords | DocType | Citations |
statistical distributions,documents processing,MapReduce,iteration process,data locality,topic selection,LDA,data mining,probability distribution,topic structure,text analysis,latent Dirichlet allocation,text mining,iterative methods,job scheduling,probabilistic topic model | Conference | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Biao Wang | 1 | 70 | 4.97 |
| yang liu | 2 | 151 | 11.93 |
| Zelong Liu | 3 | 37 | 2.86 |
| Maozhen Li | 4 | 1354 | 183.79 |
| Man Qi | 5 | 95 | 15.42 |