Temporal Data Mining via Unsupervised Ensemble Learning (Broché)

Temporal Data Mining via Unsupervised Ensemble Learning Temporal Data Mining via Unsupervised Ensemble Learning not only provides an overview of temporal data mining and an in-depth knowledge of temporal data clustering and ensemble learning techniques but also provides a rich blend of theory and practice with three proposed novel approaches. Since each temporal clustering approach favors differently structured temporal data or types of temporal data with certain assumptions, and since there is nothing universal that can solve all problems, this book enables practitioners to understand the characteristics of both clustering algorithms and the target temporal data so as to select the right approach to successfully solve each different situation.
Key Features : The first novel approach is based on the ensemble of Hidden Markov Model-based partitioning clustering, associated with a hierarchical clustering refinement, to solve problems by finding the intrinsic number of clusters and model initialization problems which exist in most model-based clustering algorithms ; The second approach presents an unsupervised ensemble learning model of iteratively constructed partitions on a sub-training set obtained by a hybrid sampling scheme which provides a potential solution for large temporal data clustering tasks ; The third proposed approach is a feature-based approach to temporal data clustering, through a weighted ensemble of a simple clustering algorithm with minimum user-dependent parameters, to address both proper grouping with minimum computational cost and provide a generic technique for the optimal solution of combining multiple partitions.
Temporal Data Mining via Unsupervised Ensemble Learning not only enumerates the existing techniques proposed so far, but also classifies and organizes them in a way that is of help for a practitioner looking for solutions to a concrete problem. The evidence suggests that ensemble learning techniques may give an optimal solution for dealing with temporal data clustering problems, and this book presents the case in an accessible format designed to appeal to both students and professional researchers, including those with little mathematical and statistical background.