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4169. Mixed Integer Programming and Heuristics Approaches for Clustering with Local Feature Selection
Invited abstract in session TC-3: Optimization and Machine Learning: Methodological Advances, stream Data Science Meets Optimization.
Tuesday, 12:30-14:00Room: 1005 (building: 202)
Authors (first author is the speaker)
| 1. | Cem Iyigün
|
| Department of Industrial Engineering, Middle East Technical University (METU) |
Abstract
In this study, we work on a clustering problem where it is assumed that the features identifying the clusters may differ for each cluster. Number of clusters and number of relevant features in each cluster are given in advance. A center-based clustering approach is proposed. Finding the cluster centers, assigning the data points and selecting relevant features for each cluster are performed simultaneously. A non-linear mixed integer mathematical model is proposed which minimizes the total distance between data points and their cluster center by using the selected features for each cluster. Different linearization methods have been used for solving the problem.
Besides, two different heuristic algorithms have been developed by taking into account the nature of the mentioned problem. Experimental results have been presented.
Keywords
- Machine Learning
- Mathematical Programming
Status: accepted
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