Cube Sampled K-Prototype Clustering for Featured Data

TL;DR

This paper introduces a probabilistic cube sampling method combined with K-Prototype clustering, utilizing PCA for inclusion probabilities, resulting in improved accuracy and reduced computation on large datasets.

Contribution

It presents a novel cube sampling technique with PCA-based inclusion probabilities integrated with K-Prototype clustering for better large-scale data clustering.

Findings

Cube sampled K-Prototype achieves higher accuracy than other sampled clustering methods.

The approach reduces computational complexity while maintaining high clustering accuracy.

Experiments on UCI datasets validate the effectiveness of the proposed method.

Abstract

Clustering large amount of data is becoming increasingly important in the current times. Due to the large sizes of data, clustering algorithm often take too much time. Sampling this data before clustering is commonly used to reduce this time. In this work, we propose a probabilistic sampling technique called cube sampling along with K-Prototype clustering. Cube sampling is used because of its accurate sample selection. K-Prototype is most frequently used clustering algorithm when the data is numerical as well as categorical (very common in today's time). The novelty of this work is in obtaining the crucial inclusion probabilities for cube sampling using Principal Component Analysis (PCA). Experiments on multiple datasets from the UCI repository demonstrate that cube sampled K-Prototype algorithm gives the best clustering accuracy among similarly sampled other popular clustering algorithms (K-Means, Hierarchical Clustering (HC), Spectral Clustering (SC)). When compared with unsampled K-Prototype, K-Means, HC and SC, it still has the best accuracy with the added advantage of reduced computational complexity (due to reduced data size).