Hierarchical clustering by aggregating representatives in sub-minimum-spanning-trees

TL;DR

This paper introduces a hierarchical clustering method that effectively identifies representative points using reciprocal nearest neighbor scoring within sub-minimum-spanning-trees, resulting in improved accuracy and scalability.

Contribution

The paper presents a novel hierarchical clustering algorithm that enhances representative point selection and demonstrates superior accuracy and efficiency over existing methods.

Findings

More accurate clustering results on UCI datasets

O(n log n) time complexity and O(log n) space complexity

Scalable to large datasets with reduced computational resources

Abstract

One of the main challenges for hierarchical clustering is how to appropriately identify the representative points in the lower level of the cluster tree, which are going to be utilized as the roots in the higher level of the cluster tree for further aggregation. However, conventional hierarchical clustering approaches have adopted some simple tricks to select the "representative" points which might not be as representative as enough. Thus, the constructed cluster tree is less attractive in terms of its poor robustness and weak reliability. Aiming at this issue, we propose a novel hierarchical clustering algorithm, in which, while building the clustering dendrogram, we can effectively detect the representative point based on scoring the reciprocal nearest data points in each sub-minimum-spanning-tree. Extensive experiments on UCI datasets show that the proposed algorithm is more accurate than other benchmarks. Meanwhile, under our analysis, the proposed algorithm has O(nlogn) time-complexity and O(logn) space-complexity, indicating that it has the scalability in handling massive data with less time and storage consumptions.