ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities

Khan, Mohammad Mahmudur Rahman; Siddique, Md. Abu Bakr; Arif, Rezoana Bente; Oishe, Mahjabin Rahman

doi:10.1109/ceeict.2018.8628138

Cited by 58 publications

(28 citation statements)

References 14 publications

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“…O(kn), k: dimension of index structure A clustering approach that implements a k-dimensional tree structure to treat the time complexity of the DBSCAN algorithm and also remove its dependence on input parameters through an automatic k-distance graph computation ADBSCAN [82] -Adaptive DBSCAN was proposed to discover clusters of varying densities in the dataset by automating the value of ε. The incremental approach is implemented in order to detect clusters at particular values of ε.…”

Section: Lsh-dbscan [81]mentioning

confidence: 99%

Comparative Analysis Review of Pioneering DBSCAN and Successive Density-Based Clustering Algorithms

Bushra

2021

IEEE Access

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The density-based spatial clustering of applications with noise (DBSCAN) is regarded as a pioneering algorithm of the density-based clustering technique. It provides the ability to handle outlier objects, detect clusters of different shapes, and disregard the need for prior knowledge about existing clusters in a dataset. These features along with its simplistic approach helped it become widely applicable in many areas of science. However, for all its accolades, the DBSCAN still has limitations in terms of performance, its ability to detect clusters of varying densities, and its dependence on user input parameters. Multiple DBSCAN-inspired algorithms have been subsequently proposed to alleviate these and more problems of the algorithm. In this paper, the implementation, features, strengths, and drawbacks of the DBSCAN are thoroughly examined. The successive algorithms proposed to provide improvement on the original DBSCAN are classified based on their motivations and are discussed. Experimental tests were conducted to understand and compare the changes presented by a C++ implementation of these algorithms compared with the original DBSCAN algorithm. Finally, the analytical evaluation presented based on the results found.

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Section: Lsh-dbscan [81]mentioning

confidence: 99%

Comparative Analysis Review of Pioneering DBSCAN and Successive Density-Based Clustering Algorithms

Bushra

2021

IEEE Access

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“…So, a new method was introduced for clusters with varying densities i.e. Adaptive type Density based on the spatial type clustering of the applications that includes the noise [28], it will identify different type of clusters with varying densities. This method outperforms existing DBSCAN algorithm.…”

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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“…Unlike unsupervised method like Fuzzy C-Means [13] and ADBSCAN [14] clustering, CNN is a supervised method. A Convolutional Neural Network (CNN) consists of an input and an output layer, as well as multiple hidden layers.…”

Section: Cnn's Architecturementioning

confidence: 99%

An Overview of Convolutional Neural Network: Its Architecture and Applications

Sakib¹,

Ahmed²,

Kabir³

et al. 2018

Preprint

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With the increase of the Artificial Neural Network (ANN), machine learning has taken a forceful twist in recent times. One of the most spectacular kinds of ANN design is the Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) is a technology that mixes artificial neural networks and up to date deep learning strategies. In deep learning, Convolutional Neural Network is at the center of spectacular advances. This artificial neural network has been applied to several image recognition tasks for decades and attracted the eye of the researchers of the many countries in recent years as the CNN has shown promising performances in several computer vision and machine learning tasks. This paper describes the underlying architecture and various applications of Convolutional Neural Network.

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ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities

Cited by 58 publications

References 14 publications

Comparative Analysis Review of Pioneering DBSCAN and Successive Density-Based Clustering Algorithms

Comparative Analysis Review of Pioneering DBSCAN and Successive Density-Based Clustering Algorithms

Density Based Spatial Clustering Application with Noise by Varying Densities

An Overview of Convolutional Neural Network: Its Architecture and Applications

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