Performance evaluation of the self-organizing map for feature extraction

AbstrakBerita merupakan sumber informasi yang dinantikan oleh manusia setiap harinya. Manusia membaca berita dengan kategori yang diinginkan. Jika komputer mampu mengelompokkan berita secara otomatis maka tentunya manusia akan lebih mudah membaca berita sesuai dengan kategori yang diinginkan. Pengelompokan berita yang berupa artikel secara otomatis sangatlah menarik karena mengorganisir artikel berita secara manual membutuhkan waktu dan biaya yang tidak sedikit.Tujuan penelitian ini adalah membuat sistem aplikasi untuk pengelompokkan artikel berita dengan menggunakan algoritma Self Organizing Map. Artikel berita digunakan sebagai input data. Kemudian sistem melakukan pemrosesan data untuk dikelompokkan. Proses yang dilakukan sistem meliputi preprocessing, feature extraction, clustering dan visualize.Sistem yang dikembangkan mampu menampilkan hasil clustering dengan algoritma Self Organizing Map dan memberikan visualisasi dengan smoothed data histograms berupa island map dari artikel berita. Selain itu sistem dapat menampilkan koleksi dokumen dari lima kategori berita yang ada pada tiap tahunnya dan banyaknya kata (histogram kata) yang sering muncul pada tiap arikel berita. Pengujian dari sistem ini dengan memasukan artikel berita, kemudian sistem memprosesnya dan mampu memberikan hasil cluster dari artikel berita yang dimasukan. Kata kunci—Pengelompokkan berita Indonesia, pengelompokkan berdasar histogram kata, pengelompokan berita menggunakan SOM Abstract News is awaited information resources by humans every day. Human reading the news with the desired category. If the computer able to news clustering with automatically, humans of course will be easier to read the news according to the desired category. News clustering in the form of news articles with automatically very interesting because it organizes news articles manually takes time and costs not a little bit.The purpose of this research is to create a system application for grouping news articles by using the Self Organizing Map algorithm. News article be used as input into the system. News articles used as input data. Then the system performs data processing until to be clustered. Processes performed by the system covers: preprocessing, feature extraction, clustering and visualize.The system developed is able to display the results clustering of the Self Organizing Map algorithm and gives visualization of the Smoothed Data Histograms in the form of island map from news articles. Additionally the system can display a word histogram and news articles from five categories news in each year. Testing of this system by entering the news articles, then the system performs data processing and gives results of a cluster from news articles that input. Keywords—Indonesia news clustering, clustering based on words histograms, news clustering using SOM

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An EEG Feature Extraction Method Based on Sparse Dictionary Self-Organizing Map for Event-Related Potential Recognition

Algorithms ◽

10.3390/a13100259 ◽

2020 ◽

Vol 13 (10) ◽

pp. 259

Author(s):

Shang Feng ◽

Haifeng Li ◽

Lin Ma ◽

Zhongliang Xu

Keyword(s):

Feature Extraction ◽

Event Related Potential ◽

The Self ◽

Computer Interface ◽

Self Organizing Map ◽

Eeg Signal ◽

Sparse Decomposition ◽

Feature Extraction Method ◽

Self Organized ◽

Self Organizing

In the application of the brain-computer interface, feature extraction is an important part of Electroencephalography (EEG) signal classification. Using sparse modeling to extract EEG signal features is a common approach. However, the features extracted by common sparse decomposition methods are only of analytical meaning, and cannot relate to actual EEG waveforms, especially event-related potential waveforms. In this article, we propose a feature extraction method based on a self-organizing map of sparse dictionary atoms, which can aggregate event-related potential waveforms scattered inside an over-complete sparse dictionary into the code book of neurons in the self-organizing map network. Then, the cosine similarity between the EEG signal sample and the code vector is used as the classification feature. Compared with traditional feature extraction methods based on sparse decomposition, the classification features obtained by this method have more intuitive electrophysiological meaning. The experiment conducted on a public auditory event-related potential (ERP) brain-computer interface dataset showed that, after the self-organized mapping of dictionary atoms, the neurons’ code vectors in the self-organized mapping network were remarkably similar to the ERP waveform obtained after superposition and averaging. The feature extracted by the proposed method used a smaller amount of data to obtain classification accuracy comparable to the traditional method.

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Predicting the risk of exotic pest species establishing in a new area using the self-organizing map, a community ecology-based approach

10.1603/ice.2016.105612 ◽

2016 ◽

Author(s):

Irene Vänninen

Keyword(s):

Community Ecology ◽

The Self ◽

Pest Species ◽

Self Organizing Map ◽

Exotic Pest ◽

Self Organizing

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The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽

10.3390/medicina57030235 ◽

2021 ◽

Vol 57 (3) ◽

pp. 235

Author(s):

Diego Galvan ◽

Luciane Effting ◽

Hágata Cremasco ◽

Carlos Adam Conte-Junior

Keyword(s):

The Self ◽

Self Organizing Map ◽

Virus Spread ◽

Self Organizing Maps ◽

The North ◽

Som Clustering ◽

Unsupervised Neural Networks ◽

Mining Tools ◽

Temporal Spread ◽

Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

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Clustering and Visualization of Large Protein Sequence Databases by Means of an Extension of the Self-Organizing Map

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/3-540-44418-1_7 ◽

2000 ◽

pp. 76-85 ◽

Cited By ~ 10

Author(s):

Panu Somervuo ◽

Teuvo Kohonen

Keyword(s):

Protein Sequence ◽

The Self ◽

Self Organizing Map ◽

Large Protein ◽

Sequence Databases ◽

Self Organizing

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VISUAL APPROACH TO SUPERVISED VARIABLE SELECTION BY SELF-ORGANIZING MAP

International Journal of Neural Systems ◽

10.1142/s0129065705000098 ◽

2005 ◽

Vol 15 (01n02) ◽

pp. 101-110 ◽

Cited By ~ 1

Author(s):

TIMO SIMILÄ ◽

SAMPSA LAINE

Keyword(s):

Variable Selection ◽

The Self ◽

Data Sets ◽

Self Organizing Map ◽

Robust Method ◽

Relevant Variables ◽

Visual Approach ◽

Predefined Criterion ◽

Target Data ◽

Self Organizing

Practical data analysis often encounters data sets with both relevant and useless variables. Supervised variable selection is the task of selecting the relevant variables based on some predefined criterion. We propose a robust method for this task. The user manually selects a set of target variables and trains a Self-Organizing Map with these data. This sets a criterion to variable selection and is an illustrative description of the user's problem, even for multivariate target data. The user also defines another set of variables that are potentially related to the problem. Our method returns a subset of these variables, which best corresponds to the description provided by the Self-Organizing Map and, thus, agrees with the user's understanding about the problem. The method is conceptually simple and, based on experiments, allows an accessible approach to supervised variable selection.

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