Unsupervised clustering is a crucial issue in data mining and pattern recognition. Based on
deep learning paradigms, deep clustering algorithms have been studied extensively and …

Spatiotemporal movement pattern discovery has stimulated considerable interest due to its
numerous applications, including data analysis, machine learning, data segmentation, data …

Uncertain data has posed a great challenge to traditional clustering algorithms. Recently,
several algorithms have been proposed for clustering uncertain data, and among them …

Clustering is a machine learning task to group similar objects in coherent sets. These groups
exhibit similar behavior with-in their cluster. With the exponential increase in the data …

Distance measure plays an important role in clustering uncertain data. However, existing
distance measures for clustering uncertain data suffer from some issues. Geometric distance …

Possible world has become one of the most effective tools to deal with various types of data
uncertainty in uncertain data management. However, few uncertain data classification …

In peer-to-peer service networks, autonomous agents gain utilities through getting services
from others. However, providing services is so costly that rational agents may prefer to defect …

There exist two severe challenges in cloud file-sharing networks: cooperation dilemma and
trust dilemma. The mechanism designed to promote cooperation could suffer from malicious …

In this thesis, we focus on structural clustering of probabilistic graphs, which comes with
significant computational challenges and has, so far, resisted efficient solutions that are able …

Both uncertain data and high-dimensional data pose huge challenges to traditional
clustering algorithms. It is even more challenging for clustering high dimensional uncertain …