Mining Information and Knowledge patterns from large databases have been recognized by many researchers as key research topic in database systems, Knowledgebase systems and in Information providing services. Clustering analysis method is one of the main analytical methods in data mining; the method of clustering algorithm will influence the clustering results directly. Clustering can be applied on database using various approaches based on distance, density, hierarchy and partition. The presence of Noise is a major problem in clustering. Noise is a data item that is not relevant to data mining. The Objective of the paper is present new algorithms for clustering techniques that handles the noise effectively. Our focus is to show the effect of noise on the performance of various types of clustering techniques and to study how noise affects the clustering process in terms of time and space. We have implemented various clustering techniques such as CURE, KMediods. We have computed time complexity and space complexity of various clustering techniques for different number of clusters. These results are presented in various visual presentations like Line Chart, Bar Chart. Then we will conclude which algorithm is more efficient to deal noise.

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Knowledge Discovery Data Mining Clustering Techniques Noise pattern recognition KMeans KMediods PAM CURE FCMeans