An Overview on Clustering Methods

TL;DR

This paper provides a comprehensive overview of clustering methods, discussing various algorithms, their benefits, applications, and limitations across multiple fields such as machine learning and bioinformatics.

Contribution

It offers a broad survey of clustering techniques, highlighting their advantages, applications, and discussing current limitations in the field.

Findings

Clustering is widely used across various scientific disciplines.

Different clustering algorithms have unique benefits and limitations.

The paper discusses the importance of distance measures in clustering.

Abstract

Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets, so that the data in each subset according to some defined distance measure. This paper covers about clustering algorithms, benefits and its applications. Paper concludes by discussing some limitations.