Cube Sampled K-Prototype Clustering for Featured Data
This work addresses the computational bottleneck in clustering large mixed-type datasets, offering an incremental improvement for data analysis applications.
The paper tackles the problem of clustering large datasets with mixed numerical and categorical features by combining cube sampling with K-Prototype clustering, using PCA to derive inclusion probabilities for sampling. Experiments on UCI datasets show it achieves the best clustering accuracy among sampled and unsampled methods while reducing computational complexity.
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).