This paper presents a new k-means type algorithm for clustering high-dimensional objects in
sub-spaces. In high-dimensional data, clusters of objects often exist in subspaces rather …

This paper presents a new method to solve the problem of clustering large and complex text
data. The method is based on a new subspace clustering algorithm that automatically …

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 …

Traditional clustering algorithms consider all of the dimensions of an input data set equally.
However, in the high dimensional data, a common property is that data points are highly …

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

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 …

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 …

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 …

This paper proposes a new method to weight subspaces in feature groups and individual
features for clustering high-dimensional data. In this method, the features of high …

In this paper, we present a particle swarm optimizer (PSO) to solve the variable weighting
problem in projected clustering of high-dimensional data. Many subspace clustering …