Recent improvements in positioning technology make massive moving object data widely
available. One important analysis is to find the moving objects that travel together. Existing …

Clustering has been widely used to identify possible structures in data and help users to
understand data in an unsupervised manner. Traditional clustering methods often provide a …

Clustering high dimensional data is an emerging research field. Subspace clustering or
projected clustering group similar objects in subspaces, ie projections, of the full space. In …

Evaluating the performance of a learning algorithm is one of the basic tasks in machine
learning and data science. In this chapter, we review commonly used performance …

Subspace clustering finds sets of objects that are homogeneous in subspaces of high-
dimensional datasets, and has been successfully applied in many domains. In recent years …

Measuring the quality of a clustering algorithm has shown to be as important as the
algorithm itself. It is a crucial part of choosing the clustering algorithm that performs best for …

Clustering, one of the most important unsupervised learning problems, is the task of dividing
a set of objects into clusters such that objects within the same cluster are similar while …

Although clustering has been studied for several decades, the fundamental problem of a
valid evaluation has not yet been solved. The sound evaluation of clustering results in …

Outlier mining is an important task for finding anomalous objects. In practice, however, there
is not always a clear distinction between outliers and regular objects as objects have …

Customers of different contract types have different shapes in daily load profiles in the
manner of different characteristics. Therefore, maximally capture local and global shape …