Journal of Theoretical and Applied Information Technology 31 st October 2016. Vol.92. No.2 © 2005 - 2016 JATIT & LLS. All rights reserved . ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 395 K-NEAREST NEIGHBOR BASED DBSCAN CLUSTERING ALGORITHM FOR IMAGE SEGMENTATION SURESH KURUMALLA 1 , P SRINIVASA RAO 2 1 Research Scholar in CSE Department, JNTUK Kakinada 2 Professor, CSE Department, Andhra University, Visakhapatnam, AP, India E-mail id: kurumallasuresh@gmail.com ABSTRACT Clustering is a primary and vital part in data mining. Density based clustering approach is one of the important technique in data mining. The groups that are designed depending on the density are flexible to understand and do not restrict itself to the outlines of clusters. DBSCAN Algorithm is one of the density grounded clustering approach which is employed in this paper. The author addressed two drawbacks of DBSCAN algorithm i.e. determination of Epsilon value and Minimum number of points and further proposed a novel efficient DBSCAN algorithm as to overcome this drawback. This proposed approach is introduced mainly for the applications on images as to segment the images very efficiently depending on the clustering algorithm. The experimental results of the suggested approached showed that the noise is highly reduced from the image and segmentations of the images are also improved better compared to the existing image segmentation approaches. Keywords: Data Mining, Clustering, Density Based Clustering, DBSCAN, K-Nearest Neighbor, Image Segmentation. 1. INTRODUCTION Emerging of current methods for logical information gathering had ensued in huge scale accretion of information relating to dissimilar arenas. Data mining is the encouraging methodology that arrives at the conclusion in the domain of computer science where it mine the vital or beneficial knowledge from enormous data samples or huge amount of data. It is a stage in the Knowledge Discovery in Databases (KDD) procedure comprising of the application for data investigation and detection of procedures under adequate computational efficacy restrictions, generates a specific enumeration of trends above the data [8]. It employs sophisticated arithmetical study and modeling methods to reveal patterns and interactions concealed in administrative data samples. Clustering plays a significant part in the data mining. The approach of identifying similarities amongst information rendering to the features obtained in the data and merging analogous data entities into groups is known as clustering. It is an unsupervised categorization of distinguishing set of identical entities in outsized data samples without having definite clusters through unambiguous features. Clustering Methods are advantageous in numerous domains like excavating skin lesion images, pattern analysis, machine learning circumstances and numerous other domains. Clustering is categorized into five kinds: Partitioned dependent, Hierarchical dependent, Density dependent, Model dependent and Grid dependent. Density dependent approaches are grounded on an approximation of the density of information. The universal perception of these approaches is that the clusters to construct are designed of a group of points of greatly density surrounded by a lower density values. In this Method, most segregating procedures group entities depending on the distance amongst entities. Such procedures could discover random designed groups. The universal perception is to endure developing the specified cluster as elongated as the density or number of entities or data values in the neighborhood beats certain threshold. These procedures could be employed to filter the noise or outliers. Density dependent clustering approach is one of the crucial procedures for grouping in data mining. The clusters that are designed depending on the density are flexible to understand and do not restrict itself to the outline of clusters. Almost all of the famous clustering procedures necessitate input parameters that are rigid to define however have a substantial effect