Comparison On Various Clustering Algorithms - 1937 Words

Comparison on various Clustering Algorithms Thejas S M.tech , Information Technology dept. of computer science and engineering National Institute of Engineering Mysuru, India thejas.055@gmail.com Pradyoth Hegde M.tech , Information Technology dept. of computer science and engineering National Institute of Engineering Mysuru, India pradyothhegde@gmail.com Abstract—The main aim is to provide a comparison of different clustering algorithm techniques in data mining. Clustering techniques is broadly used in many applications such as pattern recognition, market research, image processing and data analysis. Cluster Analysis is an excellent data mining tool for a large and multivariate database. A cluster of data objects can be treated as one group. In clustering analysis our object is first partition the set of data into similar data groups and then assigns labels to those groups. Clustering is a suitable example of unsupervised classification. Keywords—Data Mining; Clustering algorithms; Techniques; (Partition, Density Based, Hierarchical, Grid Based etc ) I. INTRODUCTION Data mining techniques are basically categorised into two major groups as Supervised learning and Unsupervised learning. Clustering is a process of grouping the similar data sets into groups. These groups should have two properties like dissimilarity between the groups and similarity within the group. Clustering is covered in the unsupervised learning category. There are no predefined class labelShow MoreRelatedData Mining Method Of Extracting The Data From Large Database1681 Words   |  7 Pagesmining is the method of extracting the data from large database. Various data mining techniques are clustering, classification, association analysis, regression, summarization, time series analysis and sequence analysis, etc. Clustering is one of the important tasks in mining and is said to be unsupervised classification. 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