Cluster Analysis

Mathur, Anisha and Vardhan, Harsha and Arjun, C V (2017) Cluster Analysis. In: International Conference on Contemporary issues in Science, Engineering & Management, 28/05/2017, Hotel Excellency, Bhubaneswar.

[img] PDF
961.pdf - Published Version
Restricted to Registered users only

Download (786kB) | Request a copy


Clustering is the method of organizing objects having similar properties in one group and those having dissimilar properties in a separate group. It is the most important form of unsupervised learning. Clustering aims at the natural grouping in a set containing unlabeled data. A good clustering is one which satisfies the user’s information needs based on the user specified criterion. Clustering can be used for data reduction, finding out data types of data members, detecting outliers etc. Clustering algorithms could be applied in various fields like marketing, insurance, studies of natural disasters, WWW, etc. There are different algorithms to deal with various types of data. To find out the best suitable algorithm for our dataset, we need to be able to compare these algorithms. This paper describes the various clustering algorithms and their applications and limitations.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Clustering, Density based clustering, Hierarchical clustering, Partitional clustering
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 11 Dec 2017 10:07
Last Modified: 11 Dec 2017 10:07

Actions (login required)

View Item View Item