A H-K Clustering Algorithm For High Dimensional Data Using Ensemble Learning

TL;DR

This paper proposes an enhanced H-K clustering algorithm that integrates subspace and ensemble clustering methods to effectively handle high-dimensional data, reducing computational complexity and improving accuracy.

Contribution

It introduces a novel hybrid clustering approach combining H-K, subspace, and ensemble clustering techniques to address high-dimensional data challenges.

Findings

Improved clustering accuracy on high-dimensional datasets.

Reduced computational complexity compared to traditional methods.

Enhanced robustness against data sparsity and curse of dimensionality.

Abstract

Advances made to the traditional clustering algorithms solves the various problems such as curse of dimensionality and sparsity of data for multiple attributes. The traditional H-K clustering algorithm can solve the randomness and apriority of the initial centers of K-means clustering algorithm. But when we apply it to high dimensional data it causes the dimensional disaster problem due to high computational complexity. All the advanced clustering algorithms like subspace and ensemble clustering algorithms improve the performance for clustering high dimension dataset from different aspects in different extent. Still these algorithms will improve the performance form a single perspective. The objective of the proposed model is to improve the performance of traditional H-K clustering and overcome the limitations such as high computational complexity and poor accuracy for high dimensional data by combining the three different approaches of clustering algorithm as subspace clustering algorithm and ensemble clustering algorithm with H-K clustering algorithm.