Clustering of the Self-Organizing Map (original) (raw)
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The Self Organizing Map as a Tool for Cluster Analysis
Menemui Matematik (Discovering Mathematics), 2016
The Self-organizing map is among the most acceptable algorithm in the unsupervised learning technique for cluster analysis. It is an important tool used to map high-dimensional data sets onto a low-dimensional discrete lattice of neurons. This feature is used for clustering and classifying data. Clustering is the process of grouping data elements into classes or clusters so that items in each class or cluster are as similar to each other as possible. In this paper, we present an overview of self organizing map, its architecture, applications and its training algorithm. Computer simulations have been analyzed based on samples of data for clustering problems.
2005
In this paper, we propose a new clustering method consisting in automated “flood- fill segmentation” of the U*-matrix of a Self-Organizing Map after training. Using several artificial datasets as a benchmark, we find that the clustering results of our U*F method are good over a wide range of critical dataset types. Furthermore, comparison to standard clustering algorithms (K-means, single-linkage and Ward) directly applied on the same datasets show that each of the latter performs very bad on at least one kind of dataset, contrary to our U*F clustering method: while not always the best, U*F clustering has the great advantage of exhibiting consistently good results. Another advantage of U*F is that the computation cost of the SOM segmentation phase is negligible, contrary to other SOM-based clustering approaches which apply O(n2logn) standard clustering algorithms to the SOM prototypes. Finally, it should be emphasized that U*F clustering does not require a priori knowledge on the nu...
A Comparison Study: Clustering using Self-Organizing Map and K-means Algorithm
2016
Nowadays clustering is applied in many different scopes of study. There are many methods that have been proposed, but the most widely used is K-means algorithm. Neural network has been also usedin clustering case, and the most popular neural network method for clustering is Self-Organizing Map (SOM). Both methods recently become the most popular and powerful one. Many scholarstry to employ and compare the performance of both mehods. Many papers have been proposed to reveal which one is outperform the other. However, until now there is no exact solution. Different scholar gives different conclusion. In this study, SOM and K-means are compared using three popular data set. Percent misclassified and output visualization graphs (separately and simultaneously with PCA) are presented to verify the comparison result.
Expanding Self-Organizing Map for data visualization and cluster analysis
Information Sciences, 2004
The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capable of projecting high-dimensional data onto a regular, usually 2-dimensional grid of neurons with good neighborhood preservation between two spaces. However, due to the dimensional conflict, the neighborhood preservation cannot always lead to perfect topology preservation. In this paper, we establish an Expanding SOM (ESOM) to preserve better topology between the two spaces. Besides the neighborhood relationship, our ESOM can detect and preserve an ordering relationship using an expanding mechanism. The computation complexity of the ESOM is comparable with that of the SOM. Our experiment results demonstrate that the ESOM constructs better mappings than the classic SOM, especially, in terms of the topological error. Furthermore, clustering results generated by the ESOM are more accurate than those obtained by the SOM.
Data Mining and Data Visualization Using Self-Organizing Map (SOM)
2017
Data min ing and data visual izati ons are becoming essential parts in inform ation techno logy in recent e ra. Wi thout the existence of data minin g techno logy, big data can be near imposs ible to be ex tracted and have to be done manually. With the aid of data mining techn ology, now information can be gathered from data sets at much shorter time. The di scovery of data visua lizati ons a lso aid s in managing data into presen table fom) that can be understood by everyone. B ig dimensions can now be reduced to help data be more understandable . In this thesis, Kohonen se lf-organiz ing map(SOM) technique is di scussed and examined for data mining and data visualizations. SOM is a neural network technique that can performs data mining, data classificat ion and data visua lizati ons. SOM Too lbox was used on MATLAB. All steps in SOM are exp la ined in detail s from weight initi a lization until trai nin g is stopped . Graphical explanations of how SOM works are a lso used to help ...
Clustering Using Adaptive Self-organizing Maps (ASOM) and Applications
Lecture Notes in Computer Science, 2005
This paper presents an innovative, adaptive variant of Kohonen's selforganizing maps called ASOM, which is an unsupervised clustering method that adaptively decides on the best architecture for the self-organizing map. Like the traditional SOMs, this clustering technique also provides useful information about the relationship between the resulting clusters. Applications of the resulting software to clustering biological data are discussed in detail.
Fast semi-automatic segmentation algorithm for Self-Organizing Maps
Self-Organizing Maps (SOM) are very powerful tools for data mining, in particular for visualizing the distribution of the data in very highdimensional data sets. Moreover, the 2D map produced by SOM can be used for unsupervised partitioning of the original data set into categories, provided that this map is somehow adequately segmented in clusters. This is usually done either manually by visual inspection, or by applying a classical clustering technique (such as agglomerative clustering) to the set of prototypes corresponding to the map. In this paper, we present a new approach for the segmentation of Self-Organizing Maps after training, which is both very simple and efficient. Our algorithm is based on a post-processing of the U-matrix (the matrix of distances between adjacent map units), which is directly derived from an elementary image-processing technique. It is shown on some simulated data sets that our partitioning algorithm appears to give very good results in terms of segmentation quality. Preliminary results on a real data set also seem to indicate that our algorithm can produce meaningful clusters on real data.
Self-organizing maps as substitutes for k-means clustering
2005
One of the most widely used clustering techniques used in GISc problems is the k-means algorithm. One of the most important issues in the correct use of k-means is the initialization procedure that ultimately determines which part of the solution space will be searched. In this paper we briefly review different initialization procedures, and propose Kohonen's Self-Organizing Maps as the most convenient method, given the proper training parameters.
SOM++: Integration of Self-Organizing Map and K-Means++ Algorithms
Machine Learning and Data Mining in Pattern Recognition, 2013
Data clustering is an important and widely used task of data mining that groups similar items together into subsets. This paper introduces a new clustering algorithm SOM++, which first uses K-Means++ method to determine the initial weight values and the starting points, and then uses Self-Organizing Map (SOM) to find the final clustering solution. The purpose of this algorithm is to provide a useful technique to improve the solution of the data clustering and data mining in terms of runtime, the rate of unstable data points and internal error. This paper also presents the comparison of our algorithm with simple SOM and K-Means + SOM by using a real world data. The results show that SOM++ has a good performance in stability and significantly outperforms three other methods training time.