International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.2, No.5, September 2012 DOI : 10.5121/ijdkp.2012.2501 1              Md. Asikur Rahman 1 , Md. Mustafizur Rahman 2 , Md. Mustafa Kamal Bhuiyan 3 , and S. M. Shahnewaz 4 1 Department of Computer Science, Memorial University of Newfoundland, St. John’s, Canada asikur.rahman@mun.ca 2 Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, Canada m.rahman@mun.ca 3 Department of Computer Science, Memorial University of Newfoundland, St. John’s, Canada mmkb65@mun.ca 4 Department of Electrical and Computer Engineering (ECE), University of Calgary, Calgary, Canada smshahne@ucalgary.ca ABSTRACT Clustering analysis is an important function of data mining. There are various clustering methods in Data Mining. Based on these methods various clustering algorithms are developed. Ant-clustering algorithm is one of such approaches that perform cluster analysis based on “Swarm Intelligence’. Existing ant- clustering algorithm uses two user defined parameters to calculate the picking-up probability and dropping probability those are used to form the cluster. But, use of user defined parameters may lead to form an inaccurate cluster. It is difficult to anticipate about the value of the user defined parameters in advance to form the cluster because of the diversified characteristics of the dataset. In this paper, we have analyzed the existing ant-clustering algorithm and then numerical analysis method of linear equation is proposed based on the characteristics of the dataset that does not need any user defined parameters to form the clusters. Results of numerical experiments on synthetic datasets demonstrate the effectiveness of the proposed method. KEYWORDS Ant-Clustering Algorithm, Swarm Intelligence, Numerical Method, Linear Equations 1. INTRODUCTION Data mining is the task of discovering interesting patterns from large amounts of data, where the data can be stored in databases, data warehouses, or other information repositories. It is a young interdisciplinary field, drawing from areas such as database systems, data warehousing, statistics, machine learning, data visualization, information retrieval, and high-performance computing. Other contributing areas include neural networks, pattern recognition, spatial data analysis, image databases, signal processing, and many application fields, such as business, economics, and bioinformatics.