Abstract: The thesis entitled “An Efficient Accumulative Constraint Based Leader Ant Clustering” is based on the Ant colony optimization clustering algorithm. Ant-based clustering can be divided into two groups. The first group of methods directly mimics the clustering behavior observed in real ant colonies. The second group is less directly inspired by nature. Clustering task can be considered as the most important unsupervised learning problems, which deals with finding a structure in a collection of unlabeled data. To this end, it conducts a process of organizing objects into groups. These algorithms have recently been shown to produce good results in a wide variety of real-world applications. In recent years, research on and with the ant-based clustering algorithms has reached a very promising state. Clustering with constraints is a developing area of machine learning, improve the efficiency of analysis and express the intractability results. It is an interactive process where a user can run the constraints number of times to refine previous clustering results. In this research, An efficient and fast constraint based Leader Ant Clustering provide three new variants algorithm (MCALA, MEALA and CEALA) are proposed that implements the following constraints: the must-link, cannot-link constraints, e –constraints and accumulative constraints. The main aim of this research is to improve the accuracy of the clustering techniques. The real data sets from the Machine Learning repository namely Glass, Iris, Wine, Thyroid and Soybean are used in this experiment. These accumulative constraints algorithms have been compared to other constraint based clustering algorithms such as K-Means clustering with constraints and the original Leader Ant clustering algorithm. The average accuracy of the proposed MCALA, MEALA and CEALA are found to be higher than the COP-K-Means, MCLA, MELA and CELA.

Keywords: clustering, constraint, artificial ants