Open AccessArtificial Neural Network Optimized Approach for Improving Spatial Cluster Quality of Land Value Zone
Author(s) -
Haviluddin Haviluddin,
Fahrul Agus,
Muhamad Taufik Azhari,
Ansari Saleh Ahmar
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.2.12738
Subject(s) - learning vector quantization , cluster analysis , centroid , artificial neural network , computer science , data mining , artificial intelligence , pattern recognition (psychology) , vector quantization , geostatistics , variance (accounting) , sample (material) , self organizing map , mathematics , statistics , spatial variability , chemistry , accounting , chromatography , business
A geostatistics practical approach is divided data sample into several groups with certain rules. Then, the data groups are used for spatial interpolation. Furthermore, clustering technique is quite commonly used in order to get distance function between sample data. In this study, Self-Organizing Maps (SOM) optimized by using Learning Vector Quantization (LVQ) especially in distance variance have been implemented. The land value zone datasets in Samarinda, East Kalimantan, Indonesia have been used. This study shows that the SOM optimized by LVQ technique have a good distance variance value in the same cluster than SOM technique. In other words, SOM-LVQ can be alternative clustering technique especially centroid position in clusters.