scholarly journals Self-Organizing Map and Multi-Layer Perceptron Neural Network Based Data Mining to Envisage Agriculture Cultivation

2008 ◽  
Vol 4 (6) ◽  
pp. 494-502 ◽  
Author(s):  
E.T. Venkatesh ◽  
Dr. P. Thangaraj
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Self Organizing Map ◽  
Multi Layer Perceptron ◽  
Self Organizing

scholarly journals Perbandingan Metode SOM/Kohonen dengan ART 2 pada Data Mining Perusahaan Retail

2017 ◽  
Vol 16 (2) ◽  
pp. 55
Author(s):  
Anak Agung Gede Bagus Ariana ◽  
I Ketut Gede Darma Putra ◽  
Linawati Linawati
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Clustering ◽  
Adaptive Resonance Theory ◽  
Self Organizing Map ◽  
Resonance Theory ◽  
Data Set ◽  
Adaptive Resonance ◽  
Clustering Data ◽  
Self Organizing

Abstract— This study investigates the performance of artificial neural network method on clustering method. Using UD. Fenny’s customer profile in year 2009 data set with the Recency, Frequency and Monetary model data. Clustering methods were compared in this study is between the Self Organizing Map and Adaptive Resonance Theory 2. The performance evaluation method validation is measured by the index cluster validation. Validation index clusters are used, among others, Davies-Bouldin index, index and index Dunn Silhouette. The test results show the method Self Organizing Map is better to process the data clustering. Index term— Data Mining, Artificial Neural Network, Self Organizing Map, Adaptive Resonance Theory 2. Intisari—Penelitian ini ingin mengetahui unjuk kerja metode clustering data berbasis jaringan saraf tiruan. Menggunakan data set profil pelanggan UD. Fenny tahun 2009 dengan atribut Recency, Frequency dan Monetary. Metode clustering yang dibandingkan pada penelitian ini adalah Self Organizing Map dan Adaptive Resonance Theory 2. Evaluasi kinerja metode dilakukan dengan mengukur validasi index dari cluster yang terbentuk. Validasi cluster yang digunakan antara lain Indeks Davies-Bouldin, Indeks Dunn dan Indeks Silhouette. Hasil pengujian menunjukkan metode Self Organizing Map lebih baik dalam melakukan proses clustering data. Kata Kunci— Data Mining, Jaringan Saraf Tiruan Self Organizing Map, Adaptive Resonance Theory 2.

Classification of salt-contaminated velocities with self-organizing map neural network

2002 ◽  
Vol 21 (12) ◽  
pp. 1193-1196 ◽  
Author(s):  
Lin Zhang ◽  
Al Fortier ◽  
David C. Bartel
Keyword(s):  
Neural Network ◽  
Self Organizing Map ◽  
Self Organizing

scholarly journals Chinese Language Feature Analysis Based on Multilayer Self-Organizing Neural Network and Data Mining Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiujin Yu ◽  
Shengfu Liu ◽  
Hui Zhang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Feature Recognition ◽  
High Efficiency ◽  
Chinese Language ◽  
Strong Support ◽  
Superior Performance ◽  
Feature Analysis ◽  
Data Mining Techniques ◽  
Self Organizing

As one of the oldest languages in the world, Chinese has a long cultural history and unique language charm. The multilayer self-organizing neural network and data mining techniques have been widely used and can achieve high-precision prediction in different fields. However, they are hardly applied to Chinese language feature analysis. In order to accurately analyze the characteristics of Chinese language, this paper uses the multilayer self-organizing neural network and the corresponding data mining technology for feature recognition and then compared it with other different types of neural network algorithms. The results show that the multilayer self-organizing neural network can make the accuracy, recall, and F1 score of feature recognition reach 68.69%, 80.21%, and 70.19%, respectively, when there are many samples. Under the influence of strong noise, it keeps high efficiency of feature analysis. This shows that the multilayer self-organizing neural network has superior performance and can provide strong support for Chinese language feature analysis.

Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques

2016 ◽  
pp. 203-214 ◽  
Author(s):  
Ahmad Al-Khasawneh
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Diagnosis ◽  
Multilayer Perceptron ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Self Organizing Map ◽  
Intelligent Information ◽  
Self Organizing

Breast cancer is the second leading cause of cancer deaths in women worldwide. Early diagnosis of this illness can increase the chances of long-term survival of cancerous patients. To help in this aid, computerized breast cancer diagnosis systems are being developed. Machine learning algorithms and data mining techniques play a central role in the diagnosis. This paper describes neural network based approaches to breast cancer diagnosis. The aim of this research is to investigate and compare the performance of supervised and unsupervised neural networks in diagnosing breast cancer. A multilayer perceptron has been implemented as a supervised neural network and a self-organizing map as an unsupervised one. Both models were simulated using a variety of parameters and tested using several combinations of those parameters in independent experiments. It was concluded that the multilayer perceptron neural network outperforms Kohonen's self-organizing maps in diagnosing breast cancer even with small data sets.

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