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Title
Abstract
Introduction
Literature Review
Clustering Methods and Techniques
Hierarchical Method
Agglomerative Method
Divisive Method
Density-Based Method
Grid-Based Method
Sting (Statistical Information Grid)
Model-Based Method
Conceptual Method
Comparative Analysis
Partitioning Method
Conclusion
References
All Topics
Computer Science
Machine Learning

A Systematic Comparative Analysis of Clustering Techniques

Profile image of shivani guptashivani guptaProfile image of Satinder Bal GuptaSatinder Bal Gupta

2020, Riga Technical University

https://doi.org/10.2478/ACSS-2020-0011
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18 pages

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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.

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