Clustering refers to the task of identifying groups or clusters in a data set. In density‐based
clustering, a cluster is a set of data objects spread in the data space over a contiguous …

Subspace clustering refers to the task of identifying clusters of similar objects or data records
(vectors) where the similarity is defined with respect to a subset of the attributes (ie, a …

Outlier mining is a major task in data analysis. Outliers are objects that highly deviate from
regular objects in their local neighborhood. Density-based outlier ranking methods score …

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 …

Mining dense subgraphs such as cliques or quasi-cliques is an important graph mining
problem and closely related to the notion of graph clustering. In various applications, graphs …

Outlier mining is an important data analysis task to distinguish exceptional outliers from
regular objects. For outlier mining in the full data space, there are well established methods …

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 …

In explorative data analysis, the data under consideration often resides in a high-
dimensional (HD) data space. Currently many methods are available to analyze this type of …

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 …

Measuring the dependence of data plays a central role in statistics and machine learning. In
this work, we summarize and generalize the main idea of existing information-theoretic …