Chapter 8

Conclusions, Future Work

The works presented in this book principally focus on solving the fundamental problems of temporal data clustering tasks in close association with ensemble-learning techniques. As described earlier, there are three methodologies for temporal data clustering; model-based clustering, proximity-based clustering, and feature-based clustering. Each approach favors differently structured temporal data or types of temporal data with certain assumptions. There is nothing universal that can solve all problems, and it is important to understand the characteristics of both clustering algorithms and the target temporal data, so that the right approach can be selected for a given clustering problem. However, there are very ...

Get Temporal Data Mining via Unsupervised Ensemble Learning now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.