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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 57581 (19)
- University: Sharif University of Technology
- Department: Computer Engineering
- Advisor(s): Jafari Siavoshani, Mahdi
- Abstract:
- The complexity of machine learning models has made it difficult for end-users and even experts in the field to understand the reasoning behind the decisions made by these models. As a result, the need for explanation and interpretation of machine learning models has been increasing. One subset of machine learning models is clustering models. Despite the extensive research conducted on interpreting supervised models, very few studies have been focused on interpreting clustering models. In this research, we aim to propose algorithms for interpreting a clustering model in a model-agnostic and post-hoc manner. In this study, various methods are presented for interpreting a clustering model. The proposed methods include both global interpretation methods and local interpretation methods. Additionally, an algorithm is introduced to evaluate different interpretations of a clustering model. The various algorithms are applied to multiple datasets, and the results are examined. Generally, it cannot be conclusively stated that one of these interpretation algorithms is superior to the others; however, the deviation-based reassignment interpretation algorithm appears to perform better than the others, as it achieved the best results on two out of three datasets.
- Keywords:
- Machine Learning ; Interpretable Machine Learning ; Explainable Clustering ; Interpretable Clustering
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