Adaptive Evolutionary Clustering

TL;DR

This paper introduces an adaptive evolutionary clustering framework that tracks time-varying proximities and adaptively estimates smoothing parameters, improving clustering performance over static and existing methods.

Contribution

It proposes a novel approach using shrinkage estimation to adaptively determine smoothing parameters, extending various static clustering algorithms into effective evolutionary clustering methods.

Findings

Outperforms static clustering in various scenarios

Outperforms existing evolutionary clustering algorithms

Effective on synthetic and real datasets

Abstract

In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naive estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.