Revised DBSCAN algorithm to cluster data with dense adjacent clusters

Tran, Tuan; Drab, Klaudia; Daszykowski, M.

doi:10.1016/j.chemolab.2012.11.006

Cited by 254 publications

(127 citation statements)

References 11 publications

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“…The Ester Martin algorithm [16] was applied to the Jungnangcheon stream data using MATLAB R16a (MathWorks, Natick, MA, USA). Our work performance fulfilled the Water 2017, 9, 220 7 of 15 conditions of CLARANS [29] and revised DBSCAN [17,30,31]. Moreover, cluster points were joined together through maximum density reachability (MDR).…”

Section: Introductionmentioning

confidence: 98%

Internet of Things-Based Arduino Intelligent Monitoring and Cluster Analysis of Seasonal Variation in Physicochemical Parameters of Jungnangcheon, an Urban Stream

Baloch

2017

Water

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Abstract:In the present case study, the use of an advanced, efficient and low-cost technique for monitoring an urban stream was reported. Physicochemical parameters (PcPs) of Jungnangcheon stream (Seoul, South Korea) were assessed using an Internet of Things (IoT) platform. Temperature, dissolved oxygen (DO), and pH parameters were monitored for the three summer months and the first fall month at a fixed location. Analysis was performed using clustering techniques (CTs), such as K-means clustering, agglomerative hierarchical clustering (AHC), and density-based spatial clustering of applications with noise (DBSCAN). An IoT-based Arduino sensor module (ASM) network with a 99.99% efficient communication platform was developed to allow collection of stream data with user-friendly software and hardware and facilitated data analysis by interested individuals using their smartphones. Clustering was used to formulate relationships among physicochemical parameters. K-means clustering was used to identify natural clusters using the silhouette coefficient based on cluster compactness and looseness. AHC grouped all data into two clusters as well as temperature, DO and pH into four, eight, and four clusters, respectively. DBSCAN analysis was also performed to evaluate yearly variations in physicochemical parameters. Noise points (NOISE) of temperature in 2016 were border points ( esent case study, the use of an advanced, efficient and low-cost technique for an stream was reported. Physicochemical parameters (PcPs) of Jungnangcheon h Korea) were assessed using an Internet of Things (IoT) platform. Temperature, DO), and pH parameters were monitored for the three summer months and the fixed location. Analysis was performed using clustering techniques (CTs), such ring, agglomerative hierarchical clustering (AHC), and density-based spatial cations with noise (DBSCAN). An IoT-based Arduino sensor module (ASM) .99% efficient communication platform was developed to allow collection of ser-friendly software and hardware and facilitated data analysis by interested their smartphones. Clustering was used to formulate relationships among arameters. K-means clustering was used to identify natural clusters using the nt based on cluster compactness and looseness. AHC grouped all data into two temperature, DO and pH into four, eight, and four clusters, respectively. was also performed to evaluate yearly variations in physicochemical parameters. E) of temperature in 2016 were border points (ƥ), whereas in 2014 and 2015 they ts (ɋ), indicating a trend toward increasing stream temperature. We found the were within the permissible limits set by the Water Quality Standards for River . o monitoring; urban stream; data analysis; clustering technique; internet of l parameters (PcPs) of a stream are critical to the health of aquatic ecosystems. edge of the temperature, pH, and dissolved oxygen content of a stream allows its ability to sustain aquatic life [1]. Aquatic ecosystem health is often impaired tivities, such as intake of urban sewage and urb...

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Section: Introductionmentioning

confidence: 98%

Internet of Things-Based Arduino Intelligent Monitoring and Cluster Analysis of Seasonal Variation in Physicochemical Parameters of Jungnangcheon, an Urban Stream

Baloch

2017

Water

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“…For a point p, the ε-neighborhood of p is the set of all the points around p within distance ε. If the number of points in the ε-neighborhood of p is no smaller than MinPts, then all the points in this set, together with p, belong to the same cluster [20]. The output of this process is the clustering map that categories the historical cases into internal and outer cases as shown in Fig.…”

Section: 2mentioning

confidence: 99%

A Constructive System Forgetting as part of Case-Based Reasoning

Mohamed

Ben-salem

Dagez

2018

int. jour. eng. com. sci

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Abstract:The human behavior has always been very influential in systems engineering. In fact, AI methods and techniques are largely influenced by the human behavior in the form of mental and mechanical capabilities. The human learning, persevering and then recalling knowledge is the focal point in artificial intelligence research. But less attention is paid to forgetting as one of the characteristics that have a very positive role in human intelligence. This paper seeks to integrate data forgetting as part of the behavior of case-based reasoning systems. The aim is to improve the performance of CBR systems by filtering out irrelevant cases as part of the machine behavior in the form of CBR systems. The paper presents a prototypical implementation of the of a forgettable CBR system that provides course recommendations as part of student registration system. Experimental work has been carried out using historical data of postgraduate students in the Computer science department, Tripoli University, Libya.

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“…DBSCAN requires two input parameters, Eps (the radius of the cluster) and MinPts (the minimum data objects required inside the cluster). In spite of its advantages, the original DBSCAN algorithm suffers by some drawbacks: (1) it is not easy to determine proper values for Eps and MinPts, (2) the computational complexity without special structure is O(n 2 ), but if a spatial index is used, the complexity can be reduced to O(nlogn) [10], and (3) it fails when the border objects of two clusters are relatively close [11], or when there are multi-density and connected clusters. There have been many efforts to mitigate the drawbacks of DB-SCAN clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Edla and Jana [16] resolve the quadratic computational complexity drawback of DBSCAN by using the prototypes produced from a squared error clustering method such as K-means. Tran et al [11] present a modified version of the DBSCAN algorithm to solve instability of DB-SCAN when detecting border objects of adjacent clusters. There are also some research to solve difficulties in finding appropriate input parameters.…”

Section: Introductionmentioning

confidence: 99%

Choosing DBSCAN Parameters Automatically using Differential Evolution

Karami¹,

Johansson²

2014

IJCA

102

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Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been widely applied in many areas of science due to its simplicity, robustness against noise (outlier) and ability to discover clusters of arbitrary shapes. However, DBSCAN algorithm requires two initial input parameters, namely Eps (the radius of the cluster) and MinPts (the minimum data objects required inside the cluster) which both have a significant influence on the clustering results. Hence, DB-SCAN is sensitive to its input parameters and it is hard to determine them a priori. This paper presents an efficient and effective hybrid clustering method, named BDE-DBSCAN, that combines Binary Differential Evolution and DBSCAN algorithm to simultaneously quickly and automatically specify appropriate parameter values for Eps and MinPts. Since the Eps parameter can largely degrades the efficiency of the DBSCAN algorithm, the combination of an analytical way for estimating Eps and Tournament Selection (TS) method is also employed. Experimental results indicate the proposed method is precise in determining appropriate input parameters of DBSCAN algorithm.

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Revised DBSCAN algorithm to cluster data with dense adjacent clusters

Cited by 254 publications

References 11 publications

Internet of Things-Based Arduino Intelligent Monitoring and Cluster Analysis of Seasonal Variation in Physicochemical Parameters of Jungnangcheon, an Urban Stream

Internet of Things-Based Arduino Intelligent Monitoring and Cluster Analysis of Seasonal Variation in Physicochemical Parameters of Jungnangcheon, an Urban Stream

A Constructive System Forgetting as part of Case-Based Reasoning

Choosing DBSCAN Parameters Automatically using Differential Evolution

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