Title :
Clustering techniques in data mining: A comparison
Author :
Garima ; Gulati, Hina ; Singh, P.K.
Author_Institution :
Amity Univ., Noida, India
Abstract :
Clustering is a technique in which a given data set is divided into groups called clusters in such a manner that the data points that are similar lie together in one cluster. Clustering plays an important role in the field of data mining due to the large amount of data sets. This paper reviews the various clustering algorithms available for data mining and provides a comparative analysis of the various clustering algorithms like DBSCAN, CLARA, CURE, CLARANS, K-Means etc.
Keywords :
data mining; pattern clustering; CLARANS; CURE; DBSCAN; clustering techniques; data mining; k-means; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Data mining; Distributed databases; Noise; Partitioning algorithms; Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH); Clustering using Representatives (CURE); Density based Clustering (DBSCAN); Distributed Density- Based Clustering (DDC); Fuzzy C Means (FCM); Ordering Point to Identify Clustering Structure (OPTICS);
Conference_Titel :
Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on
Conference_Location :
New Delhi
Print_ISBN :
978-9-3805-4415-1
