DDBSCAN: Different Densities-Based Spatial Clustering of Applications with Noise

Hassanin, Mohammad F.; Hassan, Muhammed A.; Shoeb, Abdalla

doi:10.1109/iccicct.2015.7475312

Cited by 12 publications

(6 citation statements)

References 5 publications

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“…However, it is sensitive to the window size (radius r ) on the clustering effect. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [ 32 ] also does not need to preset the number of clusters, but its performance is not good when the density of clusters is different. It needs to set the distance threshold and the number threshold, which are difficult to obtain the appropriate value; Gaussian Mixed Model's (GMM) Expectation-Maximization (EX) clustering algorithm is used to adapt to different shapes of data sets and requires a certain understanding of the clustering situation of data sets in advance.…”

Section: Literature Reviewmentioning

confidence: 99%

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

Wei

Liang

Tang

2022

Computational Intelligence and Neuroscience

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The disposal of municipal solid waste (MSW) is based on the divide-regional operation model at present, which brings management convenience. However, due to the constraints of the division between the operating regions, there are problems such as inflexible vehicle scheduling and low efficiency. Changing this mode will put pressure on management. It is necessary to break the constraint of the region and form a fully automatic operating scheme without too much manual management. First, the initial clusters are formed by considering the distance in the initial allocation module. Secondly, through the type labeling and reallocation module, the single big data set is transformed into multiple small data sets by considering the allocated amount of garbage and the carrying capacity of garbage vehicles. Then, this work proposes the improved hierarchical agglomerative clustering (IHAC) algorithm and the garbage collecting path planning (GCPP) algorithm to realize the intelligent allocation of waste and scheduling route planning of garbage vehicles. Finally, through simulation by real example and comparative analysis, the advantages of the proposed scheme are discussed. The results show that the proposed scheme is more effective than the original scheme and other advanced methods, which can provide decision support for the scientific and intelligent collection and transportation of MSW.

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Section: Literature Reviewmentioning

confidence: 99%

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

Wei

Liang

Tang

2022

Computational Intelligence and Neuroscience

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“…However, it cannot recognize clusters of different densities due to the fixed density threshold. Some DBSCANbased algorithms have been proposed to solve this problem using variable spatial radii and density thresholds (Ashour & Sunoallah, 2011;Hassanin, Hassan, & Shoeb, 2015;Scitovski & Sabo, 2019). Recently, combining the aforementioned algorithms with the nearest-neighbour (NN) method is a popular way to achieve multi-density clustering.…”

Section: Rel Ated Workmentioning

confidence: 99%

STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities

Wang

et al. 2020

Transactions in GIS

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Clustering on spatiotemporal point events with multiple types is an important step for exploratory data mining and can help us reveal the correlation of event types. In this article, we present an effective method for discovering spatiotemporal multi‐type clusters containing events with different densities and event types (STME). Particularly, the type of events in a cluster can be different, and clusters with similar densities but different internal compositions should be distinguished. We use the distance to the kth nearest neighbour to define the size of the searched neighbourhood, and expand clusters by the concept of cluster reachable, ensuring that the proportion of various types of events in the cluster remains stable. The concept of clustering priority is also proposed to make the cluster always expand from the region with the highest density, which improves the robustness of clustering. Moreover, the density of multiple types of events in clusters is estimated to discover the internal structure of clusters and further explore the correlation between events. The effectiveness of the STME algorithm is demonstrated in several simulated and real data sets, including points of interest data in Beijing and the origins and destinations of taxi trips in New York.

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“…This work is reliable, however so many internal computations are carried out to achieve better clusters. In [14], an improved version of DBSCAN algorithm, which is named as Different DensitiesBased Spatial Clustering of Applications (DDBSCAN) is presented. The DDBSCAN algorithm calculates the cluster density with respect to epsilon and min_points.…”

Section: Review Of Literaturementioning

confidence: 99%

A Novel Density Based Clustering Algorithm by Incorporating Mahalanobis Distance

Sangeetha¹,

Padikkaramu²,

Chellan³

2018

IJIES

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Data clustering is one of the active research areas, which aims to group related data together. The process of data clustering improves the data organization and enhances the user experience as well. For this sake, several clustering algorithms are proposed in the literature. However, a constant demand for a better clustering algorithm is still a basic requirement. Understanding the necessity, this paper proposes a density based clustering algorithm which is based on Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The main drawback of DBSCAN algorithm is it requires two important parameters as initial input. It is really difficult to fix the values for these parameters, as it requires some prior knowledge about the dataset. This requirement is overthrown by the proposed clustering algorithm by selecting the parameters automatically. The automated selection of parameters is achieved by analysing the dataset and it varies from dataset to dataset. This way of parameter selection improves the quality of service and produce effective clusters. The experimental results show that the proposed approach outperforms the DBSCAN algorithm in terms of purity, F-measure and entropy.

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DDBSCAN: Different Densities-Based Spatial Clustering of Applications with Noise

Cited by 12 publications

References 5 publications

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities

A Novel Density Based Clustering Algorithm by Incorporating Mahalanobis Distance

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