Unsupervised Ensemble Learning and Its Application to Temporal Data Clustering

Temporal data clustering can provide underpinning techniques for the discovery of intrinsic structures and can condense or summarize information contained in temporal data, demands made in various fields ranging from time series analysis to understanding sequential data. In the context of the treatment of data dependency in temporal data, existing temporal data clustering algorithms can be classified in three categories: model-based, temporal-proximity and feature-based clustering. However, unlike static data, temporal data have many distinct characteristics, including high dimensionality, complex time dependency, and large volume, all of which make the clustering of temporal data more challenging than conventional static data clustering. A large of number of recent studies have shown that unsupervised ensemble approaches improve clustering quality by combining multiple clustering solutions into a single consolidated clustering ensemble that has the best performance among given clustering solutions. Hence my research concentrates on ensemble learning techniques and its application for temporal data clustering tasks.