Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …

High‐dimensional data in Euclidean space pose special challenges to data mining
algorithms. These challenges are often indiscriminately subsumed under the term 'curse of …

The evaluation of unsupervised outlier detection algorithms is a constant challenge in data
mining research. Little is known regarding the strengths and weaknesses of different …

Ensembles for unsupervised outlier detection is an emerging topic that has been neglected
for a surprisingly long time (although there are reasons why this is more difficult than …

One of the most challenging aspects of clustering is validation, which is the objective and
quantitative assessment of clustering results. A number of different relative validity criteria …

Any paper proposing a new algorithm should come with an evaluation of efficiency and
scalability (particularly when we are designing methods for “big data”). However, there are …

Cluster analysis refers to a wide range of data analytic techniques for class discovery and is
popular in many application fields. To assess the quality of a clustering result, different …

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 …

In the recent years, several approaches for machine learning on the Semantic Web have
been proposed. However, no extensive comparisons between those approaches have been …

It is common practice of the outlier mining community to repurpose classification datasets
toward evaluating various detection models. To that end, often a binary classification dataset …