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This work was accepted for paper presentation at the 2022 IEEE Region 10 Conference (TENCON 2022), held virtually and in-person in Hong Kong:

• The final version of our paper (as published in the conference proceedings of TENCON 2022) can be accessed via thislink.
  • Our preprint can be accessed via thislink.
  • Our TENCON 2022 presentation slides can be accessed via thislink.
• Ourdataset of datasets is publicly released for future researchers.
• Kindly refer to0. Directory.ipynb for a guide on navigating through this repository.

If you find our work useful, please consider citing:

@INPROCEEDINGS{9978037,  author={Gonzales, Mark Edward M. and Uy, Lorene C. and Sy, Jacob Adrianne L. and Cordel, Macario O.},  booktitle={TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)},   title={Distance Metric Recommendation for k-Means Clustering: A Meta-Learning Approach},   year={2022},  pages={1-6},  doi={10.1109/TENCON55691.2022.9978037}}

This repository is also archived onZenodo.

ABSTRACT: The choice of distance metric impacts the clustering quality of centroid-based algorithms, such as$k$-means. Theoretical attempts to select the optimal metric entail deep domain knowledge, while experimental approaches are resource-intensive. This paper presents a meta-learning approach to automatically recommend a distance metric for$k$-means clustering that optimizes the Davies-Bouldin score. Three distance measures were considered: Chebyshev, Euclidean, and Manhattan. General, statistical, information-theoretic, structural, and complexity meta-features were extracted, and random forest was used to construct the meta-learning model; borderline SMOTE was applied to address class imbalance. The model registered an accuracy of 70.59%. Employing Shapley additive explanations, it was found that the mean of the sparsity of the attributes has the highest meta-feature importance. Feeding only the top 25 most important meta-features increased the accuracy to 71.57%. The main contribution of this paper is twofold: the construction of a meta-learning model for distance metric recommendation and a fine-grained analysis of the importance and effects of the meta-features on the model’s output.

INDEX TERMS: meta-learning, meta-features,$k$-means, clustering, distance metric, random forest

• Mark Edward M. Gonzales
  mark_gonzales@dlsu.edu.ph
  

• Lorene C. Uy
  lorene_c_uy@dlsu.edu.ph
  

• Jacob Adrianne L. Sy
  jacob_adrianne_l_sy@dlsu.edu.ph
  

• Dr. Macario O. Cordel, II
  macario.cordel@dlsu.edu.ph

This is the major course output in a machine learning class for master's students under Dr. Macario O. Cordel, II of the Department of Computer Technology, De La Salle University. The task is to create a ten-week investigatory project that applies machine learning to a particular research area or offers a substantial theoretical or algorithmic contribution to existing machine learning techniques.