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

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.