Azlin Ahmad

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.

Identifying the dominant species of tropical wood species using histogram intersection method

This research is addresses to determine the dominant species that located in the overlapped clusters produced by the Kohonen Self-Organizing Map (KSOM). Before, KSOM algorithm able to cluster the tropical wood species data set effectively and accurately according to the wood features, which is wood pores sizes. Unfortunately, there are seven overlapped clusters in the clustering result and this is due to similarity features among the wood species. This problem has caused difficulty in determining the separation boundary amongst clusters, where the most dominant species for every overlapped cluster is difficult to identify. As for a solution, the Histogram Intersection (HI) method is proposed in this research to solve this problem. From the experiments, the HI has proved that it can determine the dominant species of the overlapped clusters effectively resulting in an improvement of 1.12% of the classification accuracy, compared with the clustering result without HI technique. It has shown the implementation of this method has successfully solved the problem occurred.