BRB-KMeans: Enhancing Binary Data Clustering for Binary Product Quantization

Abstract

In Binary Product Quantization (BPQ), where product quantization is applied to binary data, the traditional k-majority method is used for clustering, with centroids determined based on Hamming distance and majority vote for each bit. However, this approach often leads to a degradation in clustering quality, negatively impacting BPQ's performance. To address these challenges, we introduce Binary-to-Real-and-Back K-Means (BRB-KMeans), a novel method that initially transforms binary data into real-valued vectors, performs k-means clustering on these vectors, and then converts the generated centroids back into binary data. This innovative approach significantly enhances clustering quality by leveraging the high clustering quality of k-means in the real-valued vector space, thereby facilitating future quantization for binary data. Through extensive experiments, we demonstrate that BRB-KMeans significantly enhances clustering quality and overall BPQ performance, notably outperforming traditional methods. © 2024 Owner/Author.