Title of object

Efficiency of Random Sampling Based Data Size Reduction on Computing Time and Validity of Clustering in Data Mining

Object description

In data mining, cluster analysis is one of the widely used analytics to discover existing groups in datasets. However, the traditional clustering algorithms become insufficient for the analysis of big data which have been formed with the enormous increase in the amount of collected data in recent years. Therefore, the scalability has been one of the most intensively studied research topics for clustering big data. The parallel clustering algorithms and the Map-Reduce framework based techniqu...

Language of object

English

Author(s) / Publisher

Journal of Agricultural Informatics

Discipline(s)

Computer and Library Sciences

Keywords

data reduction, random sampling, cluster analysis, external validity indices, big data, k-means clustering

Technical format

Text

Intended end-user role

Other

Learning resource type

Research

Typical age range

18Ü

Copyright - Restrictions

This resource is licensed under the license(CC-BY-NC-ND) Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported

Cost with use

No

Source code available

No