Extended Kernel Self-Organizing Map Clustering Algorithm

2009 ◽  
Author(s):  
Ning Chen ◽  
Hongyi Zhang
Keyword(s):  
Clustering Algorithm ◽  
Self Organizing Map ◽  
Self Organizing

scholarly journals A SELF-ORGANIZING MAP FOR MIXED CONTINUOUS AND CATEGORICAL DATA

2011 ◽  
pp. 24-32 ◽  
Author(s):  
Nicoleta Rogovschi ◽  
Mustapha Lebbah ◽  
Younès Bennani
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Mixed Data ◽  
Categorical Variables ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Public Data ◽  
Self Organizing

Most traditional clustering algorithms are limited to handle data sets that contain either continuous or categorical variables. However data sets with mixed types of variables are commonly used in data mining field. In this paper we introduce a weighted self-organizing map for clustering, analysis and visualization mixed data (continuous/binary). The learning of weights and prototypes is done in a simultaneous manner assuring an optimized data clustering. More variables has a high weight, more the clustering algorithm will take into account the informations transmitted by these variables. The learning of these topological maps is combined with a weighting process of different variables by computing weights which influence the quality of clustering. We illustrate the power of this method with data sets taken from a public data set repository: a handwritten digit data set, Zoo data set and other three mixed data sets. The results show a good quality of the topological ordering and homogenous clustering.

CURVE-BASED CLUSTERING OF TIME COURSE GENE EXPRESSION DATA USING SELF-ORGANIZING MAPS

2009 ◽  
Vol 07 (04) ◽  
pp. 645-661 ◽  
Author(s):  
XIN CHEN
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Time Course ◽  
Clustering Algorithm ◽  
Expression Patterns ◽  
Self Organizing Map ◽  
Expression Data ◽  
Wide Range ◽  
Self Organizing

There is an increasing interest in clustering time course gene expression data to investigate a wide range of biological processes. However, developing a clustering algorithm ideal for time course gene express data is still challenging. As timing is an important factor in defining true clusters, a clustering algorithm shall explore expression correlations between time points in order to achieve a high clustering accuracy. Moreover, inter-cluster gene relationships are often desired in order to facilitate the computational inference of biological pathways and regulatory networks. In this paper, a new clustering algorithm called CurveSOM is developed to offer both features above. It first presents each gene by a cubic smoothing spline fitted to the time course expression profile, and then groups genes into clusters by applying a self-organizing map-based clustering on the resulting splines. CurveSOM has been tested on three well-studied yeast cell cycle datasets, and compared with four popular programs including Cluster 3.0, GENECLUSTER, MCLUST, and SSClust. The results show that CurveSOM is a very promising tool for the exploratory analysis of time course expression data, as it is not only able to group genes into clusters with high accuracy but also able to find true time-shifted correlations of expression patterns across clusters.

Color segmentation of multi-colored fabrics using self-organizing-map based clustering algorithm

2016 ◽  
Vol 87 (3) ◽  
pp. 369-380 ◽  
Author(s):  
Haifang Mo ◽  
Bugao Xu ◽  
Wenbin Ouyang ◽  
Jiangqing Wang
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Color Measurement ◽  
High Threshold ◽  
Self Organizing Map ◽  
Color Patterns ◽  
Color Categories ◽  
Color Similarity ◽  
Main Color ◽  
Self Organizing

Fabric prints may contain intricate and nesting color patterns. To evaluate colors on such a fabric, regions of different colors must be measured individually. Therefore, precise separation of colored patterns is paramount in analyzing fabric colors for digital printing, and in assessing the colorfastness of a printed fabric after a laundering or abrasion process. This paper presents a self-organizing-map (SOM) based clustering algorithm used to automatically classify colors on printed fabrics and to accurately partition the regions of different colors for color measurement. The main color categories of an image are firstly identified and flagged using the SOM’s density map and U-matrix. Then, the region of each color category is located by divining the U-matrix map with an adaptive threshold, which is determined by recursively decreasing it from a high threshold until all the flagged neurons are assigned to different regions in the divided map. Finally, the regions with high color similarity are merged to avoid possible over-segmentation. Unlike many other clustering algorithms, this algorithm does not need to pre-define the number of clusters (e.g. main colors) and can automatically select a distance threshold to partition the U-matrix map. The experimental results show that the intricate color patterns can be precisely separated into individual regions representing different colors.

Validity Test of Self-Organizing Map (SOM) and K-Means Algorithm for Employee Grouping

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Titik Susilowati ◽  
Dedy Sugiarto ◽  
Is Mardianto
Keyword(s):  
Clustering Algorithm ◽  
The Self ◽  
Self Organizing Map ◽  
Number Of Clusters ◽  
Internal Validation ◽  
Validity Test ◽  
Validation Test ◽  
Silhouette Index ◽  
Employee Attendance ◽  
Self Organizing

Managing employee work discipline needs to be done to support the development of an organization. One way to make it easier to manage employee work discipline is to group employees based on their level of discipline. This study aims to group employees based on their level of discipline using the Self Organizing Map (SOM) and K-Means algorithm. This grouping begins with collecting employee attendance data, then processing attendance data where one of them is determining the parameters to be used, then ending by implementing the clustering algorithm using the SOM and K-Means algorithms. The results of grouping that have been obtained from the implementation of the SOM and K-Means algorithms are then validated using an internal validation test consisting of the Dunn Index, the Silhouette Index and the Connectivity Index to obtain the best number of clusters and algorithms. The results of the validation test obtained 3 best clusters for the level of discipline, namely the disciplinary cluster, the moderate cluster and the undisciplined cluster.

Quality Inspection with Chi-Square Automatic Interaction Detector and Self-Organizing Map

2014 ◽  
Vol 1039 ◽  
pp. 538-543
Author(s):  
Lelija Stupar ◽  
Quan Yu ◽  
Ke Sheng Wang
Keyword(s):  
Supervised Classification ◽  
Clustering Algorithm ◽  
Geometric Features ◽  
Quality Inspection ◽  
Self Organizing Map ◽  
Improve Quality ◽  
Chi Square ◽  
Automatic Interaction Detector ◽  
Self Organizing

This paper describes two methods for the industrial quality inspection: Supervised classification algorithm Chi-Square Automatic Interaction Detector (CHAID) and unsupervised clustering algorithm Self-Organizing Map (SOM). The classification and clustering are modelled in IBM software SPSS. Models’ functioning is illustrated on a wheel assembly geometric features inspection. The classifying accuracies are compared for the two methods. CHAID has shown better classifying ability than SOM, while SOM can be used to improve quality of predictor values, and therefore classifiers accuracy.

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>

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