Pheromone-based Kohonen Self-Organizing Map (PKSOM) in clustering of tropical wood species: Performance and scalability

Azlin Ahmad; Rubiyah Yusof; Yasue Mitsukura

doi:10.1109/ASCC.2015.7244589

Pheromone-based Kohonen Self-Organizing Map (PKSOM) in clustering of tropical wood species: Performance and scalability

Azlin Ahmad, Rubiyah Yusof, Yasue Mitsukura

Department of System Design Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Clustering is proposed to cluster the high dimensional data, into clusters of data that exhibit some similarities. Due to this ability, it has been chosen to solve many problems in various areas, including tropical wood species classification. It has ease the recognition process which has been done manually before. Pheromone-based Kohonen Self-Organizing Map (PKSOM) is proposed a clustering tool to cluster the wood datasets; the filtered and raw datasets. This paper discusses the performance and scalability of modified Kohonen Self-Organizing Map (KSOM), named Pheromone-based KSOM (PKSOM). The PKSOM algorithm is trained and tested using two types of wood datasets; the raw wood dataset and filtered dataset using Genetic Algorithm (GA). The results are then being compared with two conventional clustering methods; KSOM and standard K-Mean. As a conclusion, PKSOM has produced 97.5% of accuracy for the raw wood dataset and 97.55% for the filtered wood dataset, slightly higher compared to the other two methods.

Original language	English
Title of host publication	2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781479978625
DOIs	https://doi.org/10.1109/ASCC.2015.7244589
Publication status	Published - 2015 Sep 8
Event	10th Asian Control Conference, ASCC 2015 - Kota Kinabalu, Malaysia Duration: 2015 May 31 → 2015 Jun 3

Other

Other	10th Asian Control Conference, ASCC 2015
Country	Malaysia
City	Kota Kinabalu
Period	15/5/31 → 15/6/3

Keywords

Clustering
pheromone
Pheromone-Based KSOM (PKSOM)
Tropical Wood Species

ASJC Scopus subject areas

Control and Systems Engineering

Access to Document

10.1109/ASCC.2015.7244589

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Ahmad, A., Yusof, R., & Mitsukura, Y. (2015). Pheromone-based Kohonen Self-Organizing Map (PKSOM) in clustering of tropical wood species: Performance and scalability. In 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015 [7244589] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASCC.2015.7244589

Pheromone-based Kohonen Self-Organizing Map (PKSOM) in clustering of tropical wood species : Performance and scalability. / Ahmad, Azlin; Yusof, Rubiyah; Mitsukura, Yasue.

2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7244589.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ahmad, A, Yusof, R & Mitsukura, Y 2015, Pheromone-based Kohonen Self-Organizing Map (PKSOM) in clustering of tropical wood species: Performance and scalability. in 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015., 7244589, Institute of Electrical and Electronics Engineers Inc., 10th Asian Control Conference, ASCC 2015, Kota Kinabalu, Malaysia, 15/5/31. https://doi.org/10.1109/ASCC.2015.7244589

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AB - Clustering is proposed to cluster the high dimensional data, into clusters of data that exhibit some similarities. Due to this ability, it has been chosen to solve many problems in various areas, including tropical wood species classification. It has ease the recognition process which has been done manually before. Pheromone-based Kohonen Self-Organizing Map (PKSOM) is proposed a clustering tool to cluster the wood datasets; the filtered and raw datasets. This paper discusses the performance and scalability of modified Kohonen Self-Organizing Map (KSOM), named Pheromone-based KSOM (PKSOM). The PKSOM algorithm is trained and tested using two types of wood datasets; the raw wood dataset and filtered dataset using Genetic Algorithm (GA). The results are then being compared with two conventional clustering methods; KSOM and standard K-Mean. As a conclusion, PKSOM has produced 97.5% of accuracy for the raw wood dataset and 97.55% for the filtered wood dataset, slightly higher compared to the other two methods.

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