An Enhanced Density Based Spatial Clustering of Applications with Noise

Ram, Anant; Sharma, Ashish; Jalal, Anand Singh; Agrawal, Ankur; Singh, Raghuraj

doi:10.1109/iadcc.2009.4809235

Cited by 71 publications

(26 citation statements)

References 4 publications

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“…And also highly varied density data set cannot be manipulated by DBSCAN [1] [3] To overcome this problem ,VDBSCAN algorithm is derived. The above diagram represents the highly dense data points.…”

Section: Definition 2(density Reachable )mentioning

confidence: 99%

Improved varied density based spatial clustering algorithm with noise

Vijayalakshmi

Punithavalli

2010

2010 IEEE International Conference on Computational Intelligence and Computing Research

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VDBSCAN is very famous Density based clustering algorithm. Handling highly dense data point is a challenging task in clustering. VDBSCAN algorithm handles widely varied density data points well and also over comes the problem of noise and outlier. But this algorithm is depends on the input parameters Eps and Minpts. The careful selection of these input parameters plays an important role in proper clustering. We propose automatic parameter selection in VDBSCAN for perfect clustering. Synthetic data with 2-dimention is used for the experiment. The result shows that, the proposed work enhances VDBSCAN algorithm.

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Section: Definition 2(density Reachable )mentioning

confidence: 99%

Improved varied density based spatial clustering algorithm with noise

Vijayalakshmi

Punithavalli

2010

2010 IEEE International Conference on Computational Intelligence and Computing Research

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“…These enable deduction of the actual clusters. Other density based clustering techniques include VDBSCAN [15], GMDB-SCAN [16], DDSC [17], EDBSCAN [18] etc.…”

Section: Related Workmentioning

confidence: 99%

Scalable density based spatial clustering with integrated one-class SVM for noise reduction

Ahmed

Razak

2018

IJET

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Information extraction from data is one of the key necessities for data analysis. Unsupervised nature of data leads to complex computational methods for analysis. This paper presents a density based spatial clustering technique integrated with one-class SVM, a machine learning technique for noise reduction, a modified variant of DBSCAN called NRDBSCAN. Analysis of DBSCAN exhibits its major requirement of accurate thresholds, absence of which yields suboptimal results. However, identifying accurate threshold settings is unattainable. Noise is one of the major side-effects of the threshold gap. The proposed work reduces noise by integrating a machine learning classifier into the operation structure of DBSCAN. Further, the proposed technique is parallelized using Spark architecture, thereby increasing its scalability and its ability to handle large amounts of data. Experiments and comparisons with similar techniques indicate high scalability levels and high homogeneity levels in the clustering process.

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“…It produces good clustering results even when a large amount of noise is present. EDBSCAN [6] algorithm is another improvement of DBSCAN; it keeps tracks of density variation which exists within the cluster. It calculates the density variance of a core object with respect to its -neighborhood.…”

Section: Related Workmentioning

confidence: 99%

A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases

Ram¹,

Jalal²,

Jalal³

et al. 2010

IJCA

Self Cite

133

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DBSCAN is a base algorithm for density based clustering. It can detect the clusters of different shapes and sizes from the large amount of data which contains noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. In this paper, we propose a density varied DBSCAN algorithm which is capable to handle local density variation within the cluster. It calculates the growing cluster density mean and then the cluster density variance for any core object, which is supposed to be expended further, by considering density of its -neighborhood with respect to cluster density mean. If cluster density variance for a core object is less than or equal to a threshold value and also satisfying the cluster similarity index, then it will allow the core object for expansion. The experimental results show that the proposed clustering algorithm gives optimized results. General TermsDensity Based Clustering

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An Enhanced Density Based Spatial Clustering of Applications with Noise

Cited by 71 publications

References 4 publications

Improved varied density based spatial clustering algorithm with noise

Improved varied density based spatial clustering algorithm with noise

Scalable density based spatial clustering with integrated one-class SVM for noise reduction

A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases

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