Learning-Augmented K-Means Clustering Using Dimensional Reduction
Issam K. O Jabari Shofiyah Pradiptya Kahvi S Novi Nur Putriwijaya Novanto Yudistira
Learning augmented is a machine learning concept built to improve the
performance of a method or model, such as enhancing its ability to predict and
generalize data or features, or testing the reliability of the method by
introducing noise and other factors. On the other hand, clustering is a
fundamental aspect of data analysis and has long been used to understand the
structure of large datasets. Despite its long history, the k-means algorithm
still faces challenges. One approach, as suggested by Ergun et al,is to use a
predictor to minimize the sum of squared distances between each data point and
a specified centroid. However, it is known that the computational cost of this
algorithm increases with the value of k, and it often gets stuck in local
minima. In response to these challenges, we propose a solution to reduce the
dimensionality of the dataset using Principal Component Analysis (PCA). It is
worth noting that when using k values of 10 and 25, the proposed algorithm
yields lower cost results compared to running it without PCA. "Principal
component analysis (PCA) is the problem of fitting a low-dimensional affine
subspace to a set of data points in a high-dimensional space. PCA is
well-established in the literature and has become one of the most useful tools
for data modeling, compression, and visualization."