Design and development of a Spatial DBSCAN Clustering framework for location prediction- An optimization approach

Perumal, Mousi; Velumani, Bhuvaneswari

doi:10.1109/cesys.2018.8724094

Cited by 2 publications

(1 citation statement)

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“…The define two parameters epsilon and minimum points are very significant and even sometimes complex to the efficiency of clustering for the decreased set [17]. DBSCAN has successful in location prediction which the results show good deep source of water that used in the water care plant and getting the optimal cluster [18].…”

Section: Clusteringmentioning

confidence: 99%

Clustering of Human Hand on Depth Image using DBSCAN Method

Yohannes

Utaminingrum

Shih

2019

JITeCS

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In recent years, depth images are popular research in imageprocessing, especially in clustering field. The depth image can captureby depth cameras such as Kinect, Intel Real Sense, Leap Motion, and etc.Many objects and methods can be implemented in clustering field andissues. One of popular object is human hand since has many functionsand important parts of human body for daily routines. Besides, theclustering method has been developed for any goal and even combinewith another method. One of clustering method is Density-Based SpatialClustering of Applications with Noise (DBSCAN) which automaticclustering method consists of minimum points and epsilon. Define theepsilon in DBSCAN is important thing since the result depends on those.We want to look for the best epsilon for clustering human hand in thedepth images. We selected the epsilon from 5 until 100 for getting thebest clustering results. Moreover, those epsilons will be testing in threedistance to get accurate results.

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Section: Clusteringmentioning

confidence: 99%