International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 10, No. 7, 8 Jul. 2018
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An Efficient Clustering Algorithm for Spatial Datasets with Noise
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Author(s)
Akash Nag
1,*
Sunil Karforma
2
Index Terms
Clustering, data mining, spatial datasets, noisy data
Abstract
Clustering is the technique of finding useful patterns in a dataset by effectively grouping similar data items. It is an intense research area with many algorithms currently available, but practically most algorithms do not deal very efficiently with noise. Most real-world data are prone to containing noise due to many factors, and most algorithms, even those which claim to deal with noise, are able to detect only large deviations as noise. In this paper, we present a data-clustering method named SIDNAC, which can efficiently detect clusters of arbitrary shapes, and is almost immune to noise – a much desired feature in clustering applications. Another important feature of this algorithm is that it does not require apriori knowledge of the number of clusters – something which is seldom available.
Cite This Paper
Akash Nag, Sunil Karforma, " An Efficient Clustering Algorithm for Spatial Datasets with Noise", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.7, pp. 29-36, 2018. DOI:10.5815/ijmecs.2018.07.03
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