Pattern Clustering Using a Swarm Intelligence Approach Swagatam Das 1 and and Ajith Abraham 2 1 Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India. 2 Center of Excellence for Quantifiable Quality of Service Norwegian University of Science and Technology, Trondheim, Norway ajith.abraham@ieee.org Summary. Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the pro- cess of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This, in turn, imposes severe computational requirements on the relevant cluster- ing techniques. A family of bio-inspired algorithms, well-known as Swarm Intelligence (SI) has recently emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This chapter explores the role of SI in clustering different kinds of datasets. It finally describes a new SI technique for partitioning a linearly non-separable dataset into an optimal number of clusters in the kernel- induced feature space. Computer simulations undertaken in this research have also been provided to demonstrate the effectiveness of the proposed algorithm. 1 Introduction Clustering means the act of partitioning an unlabeled dataset into groups of similar objects. Each group, called a ‘cluster’, consists of objects that are similar between themselves and dis- similar to objects of other groups. In the past few decades, cluster analysis has played a central role in a variety of fields ranging from engineering (machine learning, artificial intelligence, pattern recognition, mechanical engineering, electrical engineering), computer sciences (web mining, spatial database analysis, textual document collection, image segmentation), life and medical sciences (genetics, biology, microbiology, paleontology, psychiatry, pathology), to earth sciences (geography. geology, remote sensing), social sciences (sociology, psychology, archeology, education), and economics (marketing, business) (Evangelou et al., 2001, Lille- sand and Keifer, 1994, Rao, 1971, Duda and Hart, 1973, Everitt, 1993, Xu and Wunsch, 2008). Human beings possess the natural ability of clustering objects. Given a box full of marbles of four different colors say red, green, blue, and yellow, even a child may separate these marbles into four clusters based on their colors. However, making a computer solve this type of problems is quite difficult and demands the attention of computer scientists and engineers all