<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Clustering has now become a very important tool to manage the data in many areas such as pattern recognition, machine learning, information retrieval etc. The database is increasing day by day and thus it is required to maintain the data in such a manner that useful information can easily be extracted and used accordingly. In this process, clustering plays an important role as it forms clusters of the data on the basis of similarity in data. There are more than hundred clustering methods and algorithms that can be used for mining the data but all these algorithms do not provide models for their clusters and thus it becomes difficult to categorise all of them. This paper describes the most commonly used and popular clustering techniques and also compares them on the basis of their merits, demerits and time complexity.</p></abstract>

Clustering has now become a very important tool to manage the data in many areas such as pattern recognition, machine learning, information retrieval etc. The database is increasing day by day and thus it is required to maintain the data in such a manner that useful information can easily be extracted and used accordingly. In this process, clustering plays an important role as it forms clusters of the data on the basis of similarity in data. There are more than hundred clustering methods and algorithms that can be used for mining the data but all these algorithms do not provide models for their clusters and thus it becomes difficult to categorise all of them. This paper describes the most commonly used and popular clustering techniques and also compares them on the basis of their merits, demerits and time complexity.

A Systematic Comparative Analysis of Clustering Techniques

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A Systematic Comparative Analysis of Clustering Techniques"}

Clustering has now become a very important tool to manage the data in many areas such as pattern recognition, machine learning, information retrieval etc. The...

A Systematic Comparative Analysis of Clustering Techniques

Online veröffentlicht: 28. Dez. 2020

Seitenbereich: 87 - 104

DOI: https://doi.org/10.2478/acss-2020-0011

Schlüsselwörter
Clustering, -means, data mining, fuzzy, -means, partitioning

© 2020 Satinder Bal Gupta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Systematic Comparative Analysis of Clustering Techniques

Online veröffentlicht: 28. Dez. 2020

Seitenbereich: 87 - 104

DOI: https://doi.org/10.2478/acss-2020-0011

SchlüsselwörterClustering, -means, data mining, fuzzy, -means, partitioning

© 2020 Satinder Bal Gupta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Clustering, -means, data mining, fuzzy, -means, partitioning