International Research Journal of Engineering and Technology ( I RJET) e-ISSN: 2395 -0056 Volume: 02 Issue: 04 | July-2015 www.irjet.net p-ISSN: 2395-0072 © 2015, IRJET.NET- All Rights Reserved Page 940 A HYBRI D FI REFLY BASED APPROACH FOR DATA CLUSTERI NG Gunjan Dashora 1 , Payal Awwal 2 1 Student, Computer Science, Govt. Women Engineering College,Ajmer, Rajasthan, India 2 Assistant Professor,Computer Science, Govt.Women Engineering College, Ajmer ,Rajasthan,India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In data mining, clustering or cluster analysis is a common technique. Clusters is a set of objects which are assigned into a group. The objects in a single cluster are similar to each other but they are different from the objects in other clusters. A collective behavior of social systems like insects such as ants, fish schooling ,honey bees and birds are called as swarm intelligence(SI).In this paper, a swarm intelligence based technique for data clustering is proposed using firefly and nelder mead search method .K-means algorithm tends to conver ge faster than firefly algorithm but usually trapped in a local optimal area. A new way of integrating firefly with nelder mead search method proposed in this paper. Key Wor ds: Firefly algorithm, nelder mead simplex search, swarm intelligence, K-Means. 1. INTRODUCTION The process of grouping of data into number of clusters are known as data clustering. The aim of data clustering is to keep the objects in a single cluster are similar to each other but they are different from objects in other clusters.[1]. K-means is the most popular and widely used method for clustering. In k-means , we use Euclidean distance for good clustering results.[2].However k-means contains several drawbacks like trapped into local optima and local minima and it is also sensitive to initial cluster centers.[3][4]. A new prototype Swarm Intelligence(SI) is being used in research settings to improve the management and control of large numbers of collaborating entities like computer and sensor networks, communication, satellite constellations and many more. It is a collective behavior of insects.[5] Firefly algorithm is used to overcome the problem of local optima in k-means algorithm. The firefly algorithm works on the flashing behaviour of firefly, but the convergence rate of Firefly algorithm is slower than those of local search technique( Nelder Mead simplex search). To deal with the the slow convergence of firefly ,we combine firefly with nelder- mead simplex search. 2. K-MEANS ALGORITHM Semi structured or unstructured datasets are classify with the help of k-means clustering. K-means clustering is simple and it has the ability to handle voluminous datasets. Therefore, this is the one of the most common and effective method to classify data. The parameter used in k-mean clustering is the number of clusters and the initial set of centroids. The distance of each item in the dataset is calculated with each of the centroids of the respective cluster. The item is then assigned to the cluster with which the distance of the item is least. The centroid of the cluster to which the item was assigned is recalculated. The standard k- means algorithm is as follows- Initial positions of K cluster centers are determined randomly. Following phases are repeated: a)For each data vector : the vector is allocated to a cluster which its Euclidean distance from its center is less than the other cluster centers. The distance to cluster center is calculated by Eq. (1): Dis( Xp. Zj ) = pi - Zji ) 2 (1) In Eq (1), Xp is p th data vector, Zj is j th cluster center and D is the dimension of data and cluster center. b) Cluster center are updated by Eq (2): Zj = [ ] (2) In Eq.(2), nj is the number of data vectors corresponding to j th cluster and C j is a subset of the total data vectors which constitute j th cluster and are in it. Phases (a) and (b) are repeated until stop criterion is satisfied. 3. FI REFLY ALGORI THM Short and rhythmic flashes for communication and attracting the potential hunt are used by most of the fireflies. Yang introduce this firefly algorithm in 2008[6]. Firefly works on three rhapsodize rules- 1)All fireflies are unisex, so that one firefly will be attracted to other fireflies regardless of their sex. 2)Attractiveness is proportional to their brightness. Thus, for any two flashing fireflies, the less brighter one will move towards the brighter one. The attractiveness is proportional to the brightness and they both decrease as their distance increases. If there is no brighter one than a particular firefly, it will move randomly.