How to Use Temporal-Driven Constrained Clustering to Detect Typical Evolutions
This work addresses the challenge of analyzing temporal patterns in data for domains like political studies, but it is incremental as it builds on existing clustering methods with novel constraints.
The paper tackled the problem of detecting typical evolution phases in temporal data by proposing TDCK-Means, a time-driven constrained clustering algorithm that improves temporal cohesion of clusters without significant loss in multidimensional variance, as shown in a Political Studies dataset.
In this paper, we propose a new time-aware dissimilarity measure that takes into account the temporal dimension. Observations that are close in the description space, but distant in time are considered as dissimilar. We also propose a method to enforce the segmentation contiguity, by introducing, in the objective function, a penalty term inspired from the Normal Distribution Function. We combine the two propositions into a novel time-driven constrained clustering algorithm, called TDCK-Means, which creates a partition of coherent clusters, both in the multidimensional space and in the temporal space. This algorithm uses soft semi-supervised constraints, to encourage adjacent observations belonging to the same entity to be assigned to the same cluster. We apply our algorithm to a Political Studies dataset in order to detect typical evolution phases. We adapt the Shannon entropy in order to measure the entity contiguity, and we show that our proposition consistently improves temporal cohesion of clusters, without any significant loss in the multidimensional variance.