A new density estimator based on nearest and farthest neighbor

Faroughi, Azadeh; Javidan, Reza; Emami, Mohsen

doi:10.1109/istel.2016.7881807

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…DBSCAN can discover the clusters of different shapes without any noise. Density estimator [17], uses sub groups method and assembly technique to obtain correct clusters and the computational complexity is lesser than other methods. A new clustering method for collaborative filtering [18], using dbscan it uses to improve the prediction accuracy and is proved to be more effective.A VDBSCAN [19], is same as the dbscan algorithm the only major difference between the dbscan and vdbscan is that in the vdbscan it is going to take the epsilon parameter value for the various densities that are shown in the k-distance plot.…”

Section: Related Workmentioning

confidence: 99%

Density Based Spatial Clustering Application with Noise by Varying Densities

Neerugatti*,

Moni,

Reddy A

2019

IJRTE

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Cluster algorithms are used for grouping up of similar points to form a cluster. It has seen mostly in Machine Learning algorithms. The most popular density-based algorithm is DBSCAN. DBSCAN can find the clusters, irrespective of its shapes and sizes of a cluster. DBSCAN algorithm can easily detect the noise in a clustering dataset. In the proposed algorithm we developed a model based on the existing dbscan algorithm. In the developed algorithm we focus mainly on the epsilon parameter value. Whenever the dbscan algorithm fails to form a cluster we increase the epsilon value by half of its original size. We repeat this step until a cluster is formed. Whenever a cluster is newly formed we change existing epsilon parameter value by adding the 10 percent of the previous used epsilon parameter value. We use epsilon for varying the density of a cluster. So, we can use the dbscan algorithm with the varying density values for developing a cluster. We applied this algorithm on the various datasets.

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Section: Related Workmentioning

confidence: 99%

Density Based Spatial Clustering Application with Noise by Varying Densities

Neerugatti*,

Moni,

Reddy A

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…Other works in density estimation include use of weak classifier for density estimation [29] and density estimation based on nearest and farthest neighbor [30]. to partition the data space.…”

Section: Introductionmentioning

confidence: 99%