Temporal Multinomial Mixture for Instance-Oriented Evolutionary Clustering
This work addresses evolutionary clustering for social media analysis, but it appears incremental as it extends classical mixture models with temporal smoothness.
The paper tackles the problem of capturing temporal evolution in clusters, particularly for social media data, by proposing the Temporal Multinomial Mixture (TMM) model, which shows superiority in instance-oriented clustering compared to four other probabilistic models.
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.