Tree-ART2 Learning Model for Spatial Clustering in Second Dimension

2014 ◽  
Vol 543-547 ◽  
pp. 1934-1938
Author(s):  
Ming Xiao
Keyword(s):  
Network Model ◽  
Spatial Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Adaptive Resonance Theory ◽  
Spatial Distance ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Vector Module

For a clustering algorithm in two-dimension spatial data, the Adaptive Resonance Theory exists not only the shortcomings of pattern drift and vector module of information missing, but also difficultly adapts to spatial data clustering which is irregular distribution. A Tree-ART2 network model was proposed based on the above situation. It retains the memory of old model which maintains the constraint of spatial distance by learning and adjusting LTM pattern and amplitude information of vector. Meanwhile, introducing tree structure to the model can reduce the subjective requirement of vigilance parameter and decrease the occurrence of pattern mixing. It is showed that TART2 network has higher plasticity and adaptability through compared experiments.

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.

scholarly journals A Comparative Study between of Fuzzy C-Means Algorithms and Density based Spatial Clustering of Applications with Noise

2018 ◽  
Vol 7 (3.33) ◽  
pp. 131
Author(s):  
Kwang Kyu Lee ◽  
. .
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Data Sets ◽  
Fuzzy C Means ◽  
Autonomous Decision ◽  
Density Based Clustering ◽  
New Knowledge

Data mining technology has emerged as a means of identifying patterns and trends from large amounts of data and is a computing intelligence area that provides tools for data analysis, new knowledge discovery, and autonomous decision making. Data clustering is an important problem in many areas. Fuzzy C-Means(FCM)[11,12,13] is a very important clustering technique based on fuzzy logic. DBSCAN(Density Based Spatial Clustering of Applications with Noise)[8] is a density-based clustering algorithm that is suitable for dealing with spatial data including noise and is a collection of arbitrary shapes and sizes. In this paper, we compare and analyze the performance of Fuzzy C-Means and DBSCAN algorithms in different data sets.  

WIS-ART: UNSUPERVISED CLUSTERING WITH RAM DISCRIMINATORS

1992 ◽  
Vol 03 (01) ◽  
pp. 57-63 ◽  
Author(s):  
Eamon P. Fulcher
Keyword(s):  
Random Access ◽  
Test Phase ◽  
Adaptive Resonance Theory ◽  
The Self ◽  
Hierarchical Level ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Pattern Clustering ◽  
Adaptive Pattern ◽  
System Operating

WIS-ART merges the self-organising properties of Adaptive Resonance Theory (ART) with the operation of WISARD, an adaptive pattern recognition machine which uses discriminators of conventional Random Access Memories (RAMs). The result is an unsupervised pattern clustering system operating at near real-time that implements the leader algorithm. ART’s clustering is highly dependent upon the value of a “vigilance” parameter, which is set prior to training. However, for WIS-ART hierarchical clustering is performed automatically by the partitioning of discriminators into “multi-vigilance modules”. Thus, clustering may be controlled during the test phase according to the degree of discrimination (hierarchical level) required. Methods for improving the clustering characteristics of WIS-ART whilst still retaining stability are discussed.

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