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

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 …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

As a prolific research area in data mining, subspace clustering and related problems
induced a vast quantity of proposed solutions. However, many publications compare a new …

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 …

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 …

High‐dimensional data, ie, data described by a large number of attributes, pose specific
challenges to clustering. The so‐called 'curse of dimensionality', coined originally to …

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 …

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 …

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 …