A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets

Wang, Xin; Hamilton, Howard J.

doi:10.1007/11424918_14

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…Clustering is an exploratory analysis that classifies a collection of samples into similar groups or categories [9]. Spatial clustering algorithms are classified according to different sample contents and data characteristics.…”

Section: Clustering Based On Machinesmentioning

confidence: 99%

Blackspots identification of abnormal driving behavior based on improved DBSCAN algorithm

Wang,

Zhao,

Xiu

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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In recent years, urban traffic safety issues have increasingly garnered widespread attention, as traffic accidents pose significant threats to the safety of people's lives and properties. Driver's abnormal driving behaviors often occur in conjunction with traffic accidents, and the quantity of abnormal driving behavior data is significantly larger than that of accident data, making it a more comprehensive reflection of driving conditions and deserving in-depth analysis. Existing research rarely matches abnormal driving behavior data with map coordinates, and there is limited use of visualization methods to display the distribution of data points. This paper proposes an improved DBSCAN clustering algorithm based on abnormal driving behavior data and utilizes the minimum spanning tree algorithm to calculate the density of each clustering cluster. By computing the density of the clustering clusters, we can better understand the spatial distribution characteristics of abnormal driving behaviors, further analyze the degree of clustering of these clusters, and study the distribution of abnormal driving behavior.

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Section: Clustering Based On Machinesmentioning

confidence: 99%

Blackspots identification of abnormal driving behavior based on improved DBSCAN algorithm

Wang,

Zhao,

Xiu

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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“…There is a good amount of literature available explaining the theoretical and performance aspects of various clustering algorithms. We could also find a few empirical studies comparing the quality and performance of clustering algorithms as in References 40–46. There are a few studies that cover experimental analysis on the impact of dimensionality reduction on clustering process 13,14 …”

Section: Related Workmentioning

confidence: 99%

Taxonomy grooming algorithm ‐ An autodidactic domain specific dimensionality reduction approach for fast clustering of social media text data

Renjith

Sreekumar

Jathavedan

2022

Concurrency and Computation

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Social media being the most eminent source toward the growth of big data is important for information retrieval‐based applications to improve the efficiency in proportional to the volume it must deal with. One way to achieve better performance is to upgrade the processing capacity and the alternative option is to improve the processing methodology. The latter can be achieved using smarter processing techniques and/or better algorithms. Reducing the data volume that needs to be processed is a good strategy and it can be achieved by extracting only the relevant information via user segmentation by adopting an appropriate clustering technique. However, while dealing with text content, clustering algorithms do suffer due to the very high dimensions to be dealt with. Since the domain‐specific aspects are getting lost while applying traditional dimensionality reduction approaches, it is important to device an alternate strategy. This work proposes a taxonomy grooming algorithm (TGA), an autodidactic domain‐specific dimensionality reduction approach, for fast clustering of social media text data. Our experiment results are very promising and the dimensionality reduction using TGA resulted in better results in comparison with the traditional dimensionality reduction approaches.

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“…Intense or dense regions that are accessible from each other are merged to produce clusters. Density-based clustering methods surpass at finding clusters of arbitrary shapes [9]. We here briefly present, in order, the above stated methods of clustering techniques.…”

Section: Clusteringmentioning

confidence: 99%

A Survey and Analysis of Various Agricultural Crops Classification Techniques

Chouhan¹,

Singh²,

Singh³

2016

IJCA

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Mining is way of providing and extracting some meaning information from the data so that the data can be classified and grouped easily and quickly. These mining algorithms can be applied in various fields including classification of agricultural crops production. In the fields of Data Mining various efficient algorithms are implemented for the classification of agricultural crops production. Here in this paper a survey of all the existing techniques as well as their advantages and issues are discussed. Hence by analyzing their various advantages and issues a new and efficient technique for the classification of agricultural crops production is proposed in future such as classification using Fuzzy Conclusion Tree by the Optimizing the Feature Withdrawal using PSO-SVM (Particle Swarm Optimization with Support Vector Machine).

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A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets

Cited by 12 publications

References 8 publications

Blackspots identification of abnormal driving behavior based on improved DBSCAN algorithm

Blackspots identification of abnormal driving behavior based on improved DBSCAN algorithm

Taxonomy grooming algorithm ‐ An autodidactic domain specific dimensionality reduction approach for fast clustering of social media text data

A Survey and Analysis of Various Agricultural Crops Classification Techniques

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