Temporal Multinomial Mixture for Instance-Oriented Evolutionary Clustering

TL;DR

This paper introduces a probabilistic model called Temporal Multinomial Mixture (TMM) for evolutionary clustering, effectively capturing temporal dynamics in social media data and outperforming existing models in opinion aggregation tasks.

Contribution

The paper presents TMM, a novel probabilistic clustering model that balances feature co-occurrences with temporal smoothness for instance-oriented evolutionary clustering.

Findings

TMM outperforms four other probabilistic models in case studies.

TMM effectively captures temporal evolution of social media opinions.

The model demonstrates superior clustering quality in opinion aggregation tasks.

Abstract

Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM) is an extension of classical mixture model that optimizes feature co-occurrences in the trade-off with temporal smoothness. Our model is evaluated for two recent case studies on opinion aggregation over time. We compare four different probabilistic clustering models and we show the superiority of our proposal in the task of instance-oriented clustering.