Clustering techniques in data mining: A comparison

Author

Garima ; Gulati, Hina ; Singh, P.K.

Author_Institution

Amity Univ., Noida, India

fYear

2015

fDate

11-13 March 2015

Firstpage

410

Lastpage

415

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);

fLanguage

English

Publisher

ieee

Conference_Titel

Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on

Conference_Location

New Delhi

Print_ISBN

978-9-3805-4415-1

Type

conf

Filename

7100283

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=707293