Open AccessA survey of nature-inspired algorithm for partitional data clustering
Author(s) -
S. Suresh Babu,
K. Jayasudha
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1706/1/012163
Subject(s) - cluster analysis , computer science , data mining , cure data clustering algorithm , correlation clustering , canopy clustering algorithm , constrained clustering , fuzzy clustering , data stream clustering , process (computing) , artificial intelligence , machine learning , operating system
The aim of the clustering is representing the huge amount of data objects by a smaller number of clusters or groups based on similarity. It is a task of good data analysis tool that required a rapid and precise partitioning the vast amount of data sets. The clustering problem is bring simplicity in modelling data and plays major role in the process of data mining and knowledge discovery. In the early stage, there are many conventional algorithm are used to solve the problem of data clustering. But, those conventional algorithms do not meet the requirement of clustering problem. Hence, the nature-inspired based approaches have been applied to fulfil the requirements data clustering problem and it can manage the shortcoming of conventional data clustering algorithm. This present paper is conducting a comprehensive review about the data clustering problem, discussed some of the machine learning datasets and performance metrics. This survey paper can helps to researcher in to the next steps in future.