Subspace clustering is an extension of traditional clustering that seeks to find clusters in
different subspaces within a dataset. Often in high dimensional data, many dimensions are …

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 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 …

The aim of this paper is to present a novel subspace clustering method named FINDIT.
Clustering is the process of finding interesting patterns residing in the dataset by grouping …

In order to establish consolidated standards in novel data mining areas, newly proposed
algorithms need to be evaluated thoroughly. Many publications compare a new proposition …

Finding the informative subspaces of high-dimensional datasets is at the core of numerous
applications in computer vision, where spectral-based subspace clustering is arguably the …

Subspace clustering has been investigated extensively since traditional clustering
algorithms often fail to detect meaningful clusters in high-dimensional data spaces. Many …

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 …

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 …

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 …