Self-organizing subspace clustering for high-dimensional and multi-view data

2020 ◽  
Vol 130 ◽  
pp. 253-268
Author(s):  
Aluizio F.R. Araújo ◽  
Victor O. Antonino ◽  
Karina L. Ponce-Guevara
Keyword(s):  
Subspace Clustering ◽  
High Dimensional ◽  
Self Organizing

GPU Accelerated Self-Organizing Map for High Dimensional Data

2014 ◽  
Vol 41 (3) ◽  
pp. 341-355 ◽  
Author(s):  
Yi Xiao ◽  
Rui-Bin Feng ◽  
Zi-Fa Han ◽  
Chi-Sing Leung
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Self Organizing

Self-Organizing Map and Relational Perspective Mapping for the Accurate Visualization of High-Dimensional Hyperspectral Data

2020 ◽  
Vol 92 (15) ◽  
pp. 10450-10459 ◽  
Author(s):  
Wil Gardner ◽  
Ruqaya Maliki ◽  
Suzanne M. Cutts ◽  
Benjamin W. Muir ◽  
Davide Ballabio ◽  
...  
Keyword(s):  
Hyperspectral Data ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Relational Perspective ◽  
Self Organizing

Subspace Clustering of High Dimensional Data Using Differential Evolution

2019 ◽  
pp. 47-74 ◽  
Author(s):  
Parul Agarwal ◽  
Shikha Mehta
Keyword(s):  
Differential Evolution ◽  
Distance Measure ◽  
Dimensional Space ◽  
Clustering Algorithms ◽  
High Dimensional Data ◽  
Subspace Clustering ◽  
High Dimensional ◽  
Dbscan Clustering ◽  
Evolution Algorithms ◽  
Self Adaptive

Subspace clustering approaches cluster high dimensional data in different subspaces. It means grouping the data with different relevant subsets of dimensions. This technique has become very effective as a distance measure becomes ineffective in a high dimensional space. This chapter presents a novel evolutionary approach to a bottom up subspace clustering SUBSPACE_DE which is scalable to high dimensional data. SUBSPACE_DE uses a self-adaptive DBSCAN algorithm to perform clustering in data instances of each attribute and maximal subspaces. Self-adaptive DBSCAN clustering algorithms accept input from differential evolution algorithms. The proposed SUBSPACE_DE algorithm is tested on 14 datasets, both real and synthetic. It is compared with 11 existing subspace clustering algorithms. Evaluation metrics such as F1_Measure and accuracy are used. Performance analysis of the proposed algorithms is considerably better on a success rate ratio ranking in both accuracy and F1_Measure. SUBSPACE_DE also has potential scalability on high dimensional datasets.

SPHERICAL SELF-ORGANIZING FEATURE MAP: AN INTRODUCTORY REVIEW

2006 ◽  
Vol 16 (11) ◽  
pp. 3195-3206 ◽  
Author(s):  
ARCHANA P. SANGOLE ◽  
ALEXANDROS LEONTITSIS
Keyword(s):  
Fractal Dimension ◽  
Chaotic Maps ◽  
High Dimensional Data ◽  
The Self ◽  
High Dimensional ◽  
Feature Map ◽  
Engineering Sciences ◽  
Physical Measures ◽  
Introductory Review ◽  
Self Organizing

The self-organizing feature map (SOFM) has received great attention from researchers in a variety of areas such as engineering sciences, medicine, biology and economics. The topology of these maps is usually based on 1, 2, or 3 dimensions, forming a lattice. This article discusses various aspects of the spherical SOFMs along with examples illustrating its implementation on high-dimensional data. The main advantage of the spherical SOFM is the ability to visualize complex high-dimensional data by encapsulating physical measures of the data within the 3D attributes of its spherical lattice. The article presents the potential of the spherical SOFM to visualize nonlinear data using examples of two chaotic maps, Hénon and Ikeda, with a fractal dimension of 1.2 and 1.7 respectively embedded in 2–5 dimensions.

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