  In the mining industry, many reports are generated in the project management
process. These past documents are a great resource of knowledge for future
success. However, it would be a tedious and challenging task to retrieve the
necessary information if the documents are unorganized and unstructured.
Document clustering is a powerful approach to cope with the problem, and many
methods have been introduced in past studies. Nonetheless, there is no silver
bullet that can perform the best for any types of documents. Thus, exploratory
studies are required to apply the clustering methods for new datasets. In this
study, we will investigate multiple topic modelling (TM) methods. The
objectives are finding the appropriate approach for the mining project reports
using the dataset of the Geological Survey of Queensland, Department of
Resources, Queensland Government, and understanding the contents to get the
idea of how to organise them. Three TM methods, Latent Dirichlet Allocation
(LDA), Nonnegative Matrix Factorization (NMF), and Nonnegative Tensor
Factorization (NTF) are compared statistically and qualitatively. After the
evaluation, we conclude that the LDA performs the best for the dataset;
however, the possibility remains that the other methods could be adopted with
some improvements.

Information Retrieval;Machine Learning