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An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data
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 …
sub-spaces. In high-dimensional data, clusters of objects often exist in subspaces rather …
Enhanced soft subspace clustering integrating within-cluster and between-cluster information
While within-cluster information is commonly utilized in most soft subspace clustering
approaches in order to develop the algorithms, other important information such as between …
approaches in order to develop the algorithms, other important information such as between …
A feature group weighting method for subspace clustering of high-dimensional data
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 …
features for clustering high-dimensional data. In this method, the features of high …
Subspace clustering of categorical and numerical data with an unknown number of clusters
In clustering analysis, data attributes may have different contributions to the detection of
various clusters. To solve this problem, the subspace clustering technique has been …
various clusters. To solve this problem, the subspace clustering technique has been …
Extensions of kmeans-type algorithms: A new clustering framework by integrating intracluster compactness and intercluster separation
Kmeans-type clustering aims at partitioning a data set into clusters such that the objects in a
cluster are compact and the objects in different clusters are well separated. However, most …
cluster are compact and the objects in different clusters are well separated. However, most …
Improving authorship attribution: optimizing Burrows' Delta method
PWH Smith, W Aldridge - Journal of Quantitative Linguistics, 2011 - Taylor & Francis
Abstract Burrows' Delta Method (Burrows,) is a leading method of authorship attribution. It
can be used to shortlist potential authors from a list or to even identify potential authors. The …
can be used to shortlist potential authors from a list or to even identify potential authors. The …
Score-based likelihood ratios for linguistic text evidence with a bag-of-words model
The likelihood ratio paradigm for quantifying the strength of evidence has been researched
in many fields of forensic science. Within this paradigm, score-based approaches for …
in many fields of forensic science. Within this paradigm, score-based approaches for …
Subspace Clustering of Text Documents with Feature Weighting K-Means Algorithm
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 …
data. The method is based on a new subspace clustering algorithm that automatically …
DSKmeans: a new kmeans-type approach to discriminative subspace clustering
Most of kmeans-type clustering algorithms rely on only intra-cluster compactness, ie the
dispersions of a cluster. Inter-cluster separation which is widely used in classification …
dispersions of a cluster. Inter-cluster separation which is widely used in classification …
On the use of side information for mining text data
In many text mining applications, side-information is available along with the text documents.
Such side-information may be of different kinds, such as document provenance information …
Such side-information may be of different kinds, such as document provenance information …