Multi-view learning is an emerging direction in machine learning which considers learning
with multiple views to improve the generalization performance. Multi-view learning is also …

Urban big data fusion creates huge values for urban computing in solving urban problems.
In recent years, various models and algorithms based on deep learning have been …

Previous multi-view clustering algorithms mostly partition the multi-view data in their original
feature space, the efficacy of which heavily and implicitly relies on the quality of the original …

Most existing graph-based clustering methods need a predefined graph and their clustering
performance highly depends on the quality of the graph. Aiming to improve the multiview …

In recent years, a great many methods of learning from multi-view data by considering the
diversity of different views have been proposed. These views may be obtained from multiple …

Traditional data mining usually deals with data from a single domain. In the big data era, we
face a diversity of datasets from different sources in different domains. These datasets …

Witten and Frank's textbook was one of two books that 1 used for a data mining class in the
Fall of 2001. The book covers all major methods of data mining that produce a knowledge …

Data Mining Page 1 Data Mining Practical Machine Learning Tools and Techniques Third
Edition Ian H. Witten Eibe Frank Mark A. Hall ELSEVIER AMSTERDAM • BOSTON • …

We consider clustering problems in which the available attributes can be split into two
independent subsets, such that either subset suffices for learning. Example applications of …

In many real-world tasks, there are abundant unlabeled examples but the number of labeled
training examples is limited, because labeling the examples requires human efforts and …