# Soft Semi-Supervised Deep Learning-Based Clustering
## 4 Experiments
### Results and Discussion
Some limitations of the proposed approach should be stated, which include 1. sensitivity issues toward the presetting of the number of clusters and 2. the trial-and-error approach in selecting some of the hyper-parameter values for tuning. However, to address these limitations as well as to investigate new potentials, some directions are suggested for future studies. Specifically, we suggest researching the following approaches: 1. developing an automatic determination of the number of clusters through designing an additional term to the proposed objective; 2. researching robust heuristics to guide the selection process for hyper-parameters \(\beta\) and \(\gamma\); 3.dropping the noise points and considering only the points with membership values over a certain threshold for the constraint term formulation; 4. investigating the effect of \"should-not-link\" pairwise constraints; and 5. studying the proposed method using text datasets. Conceptualization, M.S.A., M.M.B.I. and O.B. ; methodology, M.S.A., M.M.B.I. and O.B. ; software, M.S.A. ; validation, M.S.A. and M.M.B.I; formal analysis, M.S.A. and M.M.B.I. ; investigation, M.S.A. and M.M.B.I; resources, M.S.A. and M.M.B.I; data curation, M.S.A. ; writing--original draft preparation, M.S.A. ; writing--review and editing, M.S.A., M.M.B.I. and O.B. ; visualization, M.S.A. and M.M.B.I; supervision, O.B. and M.M.B.I. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. ## References

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