Weighted Clustering

TL;DR

This paper introduces simple properties that distinguish clustering paradigms, highlighting when center-based methods outperform linkage-based algorithms, especially regarding sensitivity to element weights.

Contribution

It provides formal properties that differentiate clustering methods, emphasizing the advantages of center-based approaches over linkage-based ones in certain scenarios.

Findings

Properties clearly distinguish clustering paradigms

Center-based methods are advantageous for weighted data

Sensitivity to element frequency is a key differentiator

Abstract

One of the most prominent challenges in clustering is "the user's dilemma," which is the problem of selecting an appropriate clustering algorithm for a specific task. A formal approach for addressing this problem relies on the identification of succinct, user-friendly properties that formally capture when certain clustering methods are preferred over others. Until now these properties focused on advantages of classical Linkage-Based algorithms, failing to identify when other clustering paradigms, such as popular center-based methods, are preferable. We present surprisingly simple new properties that delineate the differences between common clustering paradigms, which clearly and formally demonstrates advantages of center-based approaches for some applications. These properties address how sensitive algorithms are to changes in element frequencies, which we capture in a generalized setting where every element is associated with a real-valued weight.