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 …

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 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 …

Given a very large moderate-to-high dimensionality dataset, how could one cluster its
points? For datasets that don't fit even on a single disk, parallelism is a first class option. In …

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 …

To gain insight into today's large data resources, data mining provides automatic
aggregation techniques. Clustering aims at grou** data such that objects within groups …

Big data now is a common term. However, the evolution of big data comes from twofold. The
creation of the computer in the 1940s gradually provides tools for human beings to collect …

Projected and subspace clustering algorithms search for clusters of points in subsets of
attributes. Projected clustering computes several disjoint clusters, plus outliers, so that each …

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 …