HDGSOM: A Modified Growing Self-Organizing Map for High Dimensional Data Clustering

2005 ◽  
Author(s):  
R. Amarasiri ◽  
D. Alahakoon ◽  
K.A. Smith
Keyword(s):  
Data Clustering ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Self Organizing Map ◽  
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

scholarly journals Evolution of SOMs’ Structure and Learning Algorithm: From Visualization of High-Dimensional Data to Clustering of Complex Data

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 109 ◽  
Author(s):  
Marian B. Gorzałczany ◽  
Filip Rudziński
Keyword(s):  
Data Visualization ◽  
Data Clustering ◽  
Learning Algorithm ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Data Sets ◽  
Complex Data ◽  
Self Organizing Maps ◽  
Grid Networks ◽  
Self Organizing

In this paper, we briefly present several modifications and generalizations of the concept of self-organizing neural networks—usually referred to as self-organizing maps (SOMs)—to illustrate their advantages in applications that range from high-dimensional data visualization to complex data clustering. Starting from conventional SOMs, Growing SOMs (GSOMs), Growing Grid Networks (GGNs), Incremental Grid Growing (IGG) approach, Growing Neural Gas (GNG) method as well as our two original solutions, i.e., Generalized SOMs with 1-Dimensional Neighborhood (GeSOMs with 1DN also referred to as Dynamic SOMs (DSOMs)) and Generalized SOMs with Tree-Like Structures (GeSOMs with T-LSs) are discussed. They are characterized in terms of (i) the modification mechanisms used, (ii) the range of network modifications introduced, (iii) the structure regularity, and (iv) the data-visualization/data-clustering effectiveness. The performance of particular solutions is illustrated and compared by means of selected data sets. We also show that the proposed original solutions, i.e., GeSOMs with 1DN (DSOMs) and GeSOMS with T-LSs outperform alternative approaches in various complex clustering tasks by providing up to 20 % increase in the clustering accuracy. The contribution of this work is threefold. First, algorithm-oriented original computer-implementations of particular SOM’s generalizations are developed. Second, their detailed simulation results are presented and discussed. Third, the advantages of our earlier-mentioned original solutions are demonstrated.

A SOM PROJECTION TECHNIQUE WITH THE GROWING STRUCTURE FOR VISUALIZING HIGH-DIMENSIONAL DATA

2003 ◽  
Vol 13 (05) ◽  
pp. 353-365 ◽  
Author(s):  
ZHENG WU ◽  
GARY G. YEN
Keyword(s):  
Projection Method ◽  
Cell Structure ◽  
High Dimensional Data ◽  
Network Size ◽  
High Dimensional ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Growing Cell ◽  
Self Organizing

The Self-Organizing Map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, an intuitive and effective SOM projection method is proposed for mapping high-dimensional data onto the two-dimensional grid structure with a growing self-organizing mechanism. In the learning phase, a growing SOM is trained and the growing cell structure is used as the baseline framework. In the ordination phase, the new projection method is used to map the input vector so that the input data is mapped to the structure of the SOM without having to plot the weight values, resulting in easy visualization of the data. The projection method is demonstrated on four different data sets, including a 118 patent data set and a 399 checical abstract data set related to polymer cements, with promising results and a significantly reduced network size.

scholarly journals An improved Kohonen self-organizing map clustering algorithm for high-dimensional data sets

2021 ◽  
Vol 24 (1) ◽  
pp. 600
Author(s):  
Momotaz Begum ◽  
Bimal Chandra Das ◽  
Md. Zakir Hossain ◽  
Antu Saha ◽  
Khaleda Akther Papry
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
High Dimensional Data ◽  
Predictive Performance ◽  
High Dimensional ◽  
Data Sets ◽  
Self Organizing Map ◽  
Distance Measurements ◽  
Cancer Data ◽  
Self Organizing

<p>Manipulating high-dimensional data is a major research challenge in the field of computer science in recent years. To classify this data, a lot of clustering algorithms have already been proposed. Kohonen self-organizing map (KSOM) is one of them. However, this algorithm has some drawbacks like overlapping clusters and non-linear separability problems. Therefore, in this paper, we propose an improved KSOM (I-KSOM) to reduce the problems that measures distances among objects using EISEN Cosine correlation formula. So far as we know, no previous work has used EISEN Cosine correlation distance measurements to classify high-dimensional data sets. To the robustness of the proposed KSOM, we carry out the experiments on several popular datasets like Iris, Seeds, Glass, Vertebral column, and Wisconsin breast cancer data sets. Our proposed algorithm shows better result compared to the existing original KSOM and another modified KSOM in terms of predictive performance with topographic and quantization error.</p>

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
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