Self-Organizing Map and other clustering Algorithms

2007 ◽  
pp. 231-250
Keyword(s):  
Clustering Algorithms ◽  
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.

Self-Organizing Map and clustering algorithms for the analysis of occupational accident databases

2011 ◽  
Vol 49 (8-9) ◽  
pp. 1215-1230 ◽  
Author(s):  
Federica Palamara ◽  
Federico Piglione ◽  
Norberto Piccinini
Keyword(s):  
Clustering Algorithms ◽  
Occupational Accident ◽  
Self Organizing Map ◽  
Self Organizing

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.

Using Self-Organizing Map and Clustering to Investigate Problem-Solving Patterns in the Massive Open Online Course: An Exploratory Study

2018 ◽  
Vol 57 (2) ◽  
pp. 471-490 ◽  
Author(s):  
Youngjin Lee
Keyword(s):  
Problem Solving ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Online Course ◽  
Analysis Tool ◽  
Self Organizing Map ◽  
Multiple Perspectives ◽  
Massive Open Online ◽  
Massive Open Online Course ◽  
Self Organizing

This study investigated whether clustering can identify different groups of students enrolled in a massive open online course (MOOC). This study applied self-organizing map and hierarchical clustering algorithms to the log files of a physics MOOC capturing how students solved weekly homework and quiz problems to identify clusters of students showing similar problem-solving patterns. The usefulness of the identified clusters was verified by examining various characteristics of students such as number of problems students attempted to solve, weekly and daily problem completion percentages, and whether they earned a course certificate. The findings of this study suggest that the clustering technique utilizing self-organizing map and hierarchical clustering algorithms in tandem can be a useful exploratory data analysis tool that can help MOOC instructors identify similar students based on a large number of variables and examine their characteristics from multiple perspectives.

scholarly journals An Extended Growing Self-Organizing Map for Selection of Clusters in Sensor Networks

2005 ◽  
Vol 1 (2) ◽  
pp. 227-243 ◽  
Author(s):  
Siddeswara Mayura Guru ◽  
Arthur Hsu ◽  
Saman Halgamuge ◽  
Saman Fernando
Keyword(s):  
Sensor Networks ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Base Station ◽  
Design Tool ◽  
Sensor Nodes ◽  
Self Organizing Map ◽  
Large Area ◽  
Computing Technique ◽  
Self Organizing

Sensor networks consist of wireless enabled sensor nodes with limited energy. As sensors could be deployed in a large area, data transmitting and receiving are energy consuming operations. One of the methods to save energy is to reduce the communication distance of each node by grouping them in to clusters. Each cluster will have a cluster-head (CH), which will communicate with all the other nodes of that cluster and transmit the data to the remote base station. In this paper, we propose an extension to Growing Self-Organizing Map (GSOM) and describe the use of evolutionary computing technique to cluster wireless sensor nodes and to identify the cluster-heads. We compare the proposed method with clustering solutions based on Genetic Algorithm (GA), an extended version of Particle Swarm Optimisation (PSO) and four general purpose clustering algorithms. This could help to discover the clusters to reduce the communication energy used to transmit data when exact locations of all sensors are known and computational resources are centrally available. This method is useful in the applications where sensors are deployed in a controlled environment and are not moving. We have derived an energy minimisation model that is used as a criterion for clustering. The proposed method can also be used as a design tool to study and analyze the cluster formation for a given node placement.

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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