Sep 1, 2013 · A method to estimate the appropriate number of latent topics for source code. Using pairwise relationships between code fragments for conceptual analysis.
Abstract. Latent Dirichlet Allocation (LDA) is a data clustering algorithm that performs especially well for text documents.
This study investigated how weighting terms from different locations in source code can improve a latent Dirichlet allocation (LDA)‐based FLT.
We use a heuristic to evaluate the ability of the model to identify related source code blocks, and demonstrate the consequences of choosing too few or too many ...
We have presented a method for estimating the optimal number of topics in a latent model by using two heuristics. In many cases, this method identifies a clear ...
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How do you choose the number of topics in LDA?
Dec 1, 2015 · Based on analysis of variation of statistical perplexity during topic modelling, a heuristic approach is proposed in this study to estimate the ...
Several ad hoc, heuristic methods for selecting the proper number of topics have been proposed.
In this paper, we use Latent Dirichlet Allocation (LDA) [5] as a statistical model to infer an appropriate number of latent topics needed to optimize the topic ...
Using heuristics to estimate an appropriate number of latent topics in source code analysis. Science of Computer Programming, http://dx.doi.org/10.1016/j ...
Sep 25, 2015 · Latent Dirichlet Allocation (LDA) is the most commonly used topic modelling method across a wide number of technical fields. However, model ...