Mohammad Babrdel Bonab

  • Citations Per Year
Learn More
The K-Means Clustering Approach is one of main algorithms in the literature of Pattern recognition and Machine Learning. Yet, due to the random selection of cluster centers and the adherence of results to initial cluster centers, the risk of trapping into local optimality ever exists. In this paper, inspired by a genetic algorithm which is based on the(More)
This paper addresses the issue of extracting textural feature for timber defect detection. Statistical features based on spatial dependence matrix are extracted for both classes; clear wood and defect. Instead of using the classical directional matrices, rotation invariant spatial dependence matrix formulation is applied to ensure accurate detection(More)
Data clustering is one of widely used methods for data mining. The k-means approach is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. But some hindrances such as the sensitivity to initial values and cluster centers or the risk of trapping in local optimal reduce its best performance. The purpose of kmeans(More)
  • 1