The Implementation of Ant Clustering Algorithm (ACA) in Clustering and Classifying the Tropical Wood Species

Author

Ahmad, Ayaz ; Yusof, Rubiyah

Author_Institution

Fac. of Comput. & Math. Sci., Univ. Teknol. MARA (UiTM), Shah Alam, Malaysia

fYear

2013

fDate

2-5 Dec. 2013

Firstpage

720

Lastpage

725

Abstract

The Ant Clustering Algorithm (ACA) is a biological inspired data clustering technique, which aimed to cluster and classify the data patterns into different groups. This paper shows how the Ant Clustering Algorithm (ACA) is implemented in clustering and classifying the tropical wood species. As for feature extraction in this research, two feature extractors are selected to extract wood features from wood images, which are Basic Grey Level Aura Matrices (BGLAM) and Statistical Properties of Pores Distribution (SPPD). The ACA algorithm is then been applied in wood data training and testing, and as a result, it is proven that the ACA algorithm can cluster and classify the tropical wood data accurately and effectively.

Keywords

agriculture; ant colony optimisation; feature extraction; forestry; image classification; pattern clustering; ACA algorithm; BGLAM feature; SPPD feature; ant clustering algorithm; basic grey level aura matrices; data classification technique; data clustering technique; feature extraction; statistical properties of pores distribution; tropical wood species; wood data testing; wood data training; wood features; wood images; Accuracy; Biology; Classification algorithms; Clustering algorithms; Feature extraction; Indexes; Training; Ant Clustering Algorithm; Clustering; Tropical Wood Species;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal-Image Technology & Internet-Based Systems (SITIS), 2013 International Conference on

Conference_Location

Kyoto

Type

conf

DOI

10.1109/SITIS.2013.117

Filename

6727267