Unsupervised Neural Network Adaptive Resonance Theory 2 for Clustering
| Main Authors: | Musdholifah, Aina, Yustisia, Difla |
|---|---|
| Format: | Article NonPeerReviewed application/pdf |
| Bahasa: | eng |
| Terbitan: |
, 2010
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| Subjects: | |
| Online Access: |
https://repository.ugm.ac.id/101184/1/289.PDF https://repository.ugm.ac.id/101184/ |
Daftar Isi:
- Clustering is one of pattern recognition techniques which are often used for extract information from large amount data set to get mo re benefit for the data owner. Clustering which is an unsupervised technique assigns the input data into clusters based on their similarity degrees. In this paper, the Unsupervised Neural Network Adaptive Resonance Theory 2 is used for clustering data. To evaluate the results, a technique called Cohesion and Separation is utilized. Furthermore, in order to validate the algorithm, this study uses patient data. The proposed ART2 algorithms and validation techniques scale well and gain considerable performance due to the resulted cluster.