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

Azlin Ahmad, Rubiyah Yusof, Yasue Mitsukura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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 languageEnglish
Title of host publication2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479978625
Publication statusPublished - 2015 Sep 8
Event10th Asian Control Conference, ASCC 2015 - Kota Kinabalu, Malaysia
Duration: 2015 May 312015 Jun 3


Other10th Asian Control Conference, ASCC 2015
CityKota Kinabalu


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

ASJC Scopus subject areas

  • Control and Systems Engineering

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