Introduction to Clustering Algorithms and Applications

08/20/2014
by   Liangde Tao, et al.
0

Data clustering is the process of identifying natural groupings or clusters within multidimensional data based on some similarity measure. Clustering is a fundamental process in many different disciplines. Hence, researchers from different fields are actively working on the clustering problem. This paper provides an overview of the different representative clustering methods. In addition, application of clustering in different field is briefly introduced.

READ FULL TEXT
research
10/06/2019

Weighted Clustering Ensemble: A Review

Clustering ensemble has emerged as a powerful tool for improving both th...
research
02/14/2022

Homogenous and Heterogenous Parallel Clustering: An Overview

Recent advances in computer architecture and networking opened the oppor...
research
06/08/2020

An Algorithmic Introduction to Clustering

This paper tries to present a more unified view of clustering, by identi...
research
02/09/2022

Application of the Affinity Propagation Clustering Technique to obtain traffic accident clusters at macro, meso, and micro levels

Accident grouping is a crucial step in identifying accident-prone locati...
research
11/23/2022

A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application

Graph clustering, which aims to divide the nodes in the graph into sever...
research
02/16/2013

Clustering validity based on the most similarity

One basic requirement of many studies is the necessity of classifying da...
research
07/21/2016

Admissible Hierarchical Clustering Methods and Algorithms for Asymmetric Networks

This paper characterizes hierarchical clustering methods that abide by t...

Please sign up or login with your details

Forgot password? Click here to reset