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Reduce Noise in K-Mean Clustering using DBSCAN Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 9 |
| Year of Publication: 2022 |
| Authors: Manjur Ahammad, Faija Juhin, Dewan Md. Farid |
10.5120/ijca2022922064
|
Manjur Ahammad, Faija Juhin, Dewan Md. Farid . Reduce Noise in K-Mean Clustering using DBSCAN Algorithm. International Journal of Computer Applications. 184, 9 ( Apr 2022), 21-25. DOI=10.5120/ijca2022922064
Abstract
The growth of data mining procedure is increasing day by day. We can extract useful insight from data. For mining data different techniques and tools have been introduced every day. By gaining knowledge from those insight of the data many research paper is being written. Based on the behavior, pattern and the characteristics data are being clustered into different groups. For clustering these massive amount of data we use different types of algorithms and techniques. The most common types of algorithms that are used in clustering are partitioning, hierarchical, grid-based and model-based algorithms. To handle these data another, type of algorithms are K-means clustering, density-based algorithm, similarity-based algorithms etc. the agenda off these algorithms are different. Some performs well for nominal data, some for categorical or ordinal data, contrariwise some can remove duplicate or noisy data and some can’t do so. In this paper a method has been showed that how can we cluster a dataset and remove the noisiness of that particular dataset at the same time.
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