Density Based Clustering: Alternatives to DBSCAN

Braune, Christian; Besecke, Stephan; Kruse, Rudolf

doi:10.1007/978-3-319-09259-1_6

Cited by 18 publications

(9 citation statements)

References 21 publications

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“…The method considers multiple labels per cluster, computing a cluster validity measure from the relationships among neighbors. In [22] a density-based algorithm, similar to DBSCAN [23], is proposed as potential solution to perform multi-label clustering.…”

Section: Main Mll Tasksmentioning

confidence: 99%

Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository

et al. 2018

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New proposals in the field of multi-label learning algorithms have been growing in number steadily over the last few years. The experimentation associated with each of them always goes through the same phases: selection of datasets, partitioning, training, analysis of results and, finally, comparison with existing methods. This last step is often hampered since it involves using exactly the same datasets, partitioned in the same way and using the same validation strategy. In this paper we present a set of tools whose objective is to facilitate the management of multi-label datasets, aiming to standardize the experimentation procedure. The two main tools are an R package, mldr.datasets, and a web repository with datasets, Cometa. Together, these tools will simplify the collection of datasets, their partitioning, documentation and export to multiple formats, among other functions. Some tips, recommendations and guidelines for a good experimental analysis of multi-label methods are also presented.

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Section: Main Mll Tasksmentioning

confidence: 99%

Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository

et al. 2018

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“…46, which can be used to forecast events or depict frequent modes for sequentially, temporal-ordered datasets. The second learning method is called DBSCAN, 47 which can be used to¯nd clusters. The authors also utilized the fault recovery technique 48 that recovers the system to barrier-free state predictably.…”

Section: Slcasmentioning

confidence: 99%

A Short Review of Security-Aware Techniques in Real-Time Embedded Systems

Chai

Zhang

Zhou

et al. 2018

J CIRCUIT SYST COMP

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With the rapid development of embedded systems, users and services have been greatly facilitated while also experiencing security threats as a result of cyber-attacks and system vulnerabilities. Currently, the real-time embedded system (RTES) focus is to deal with these security issues. In this paper, we introduce a short review of security-aware techniques for RTES. We mainly discuss two common approaches to improve the security of RTESs. The first approach is achieved by exploring specific attacks. The second approach is realized by deploying security-guaranteed services. However, improving the security of embedded systems may cause excessive energy consumption at the same time. Therefore, we investigate the secure and energy-aware RTESs on a wide range of research. In addition, we study a number of common applications used in secure RETSs. This paper stands for providing awareness and better understanding of the current RTES research status as well as technical theory behind it. Hence, the RTES security issues are resolved.

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“…Density‐based clustering creates clusters using areas of high density separated by other areas of lower density . For instance, Andrade et al presented a GPU implementation of DBSCAN, a density‐based algorithm which innovates on the use of an efficient data indexing using graphs, achieving speedups over 100 times than its sequential CPU version.…”

Section: Data Mining Tasks and Techniquesmentioning

confidence: 99%

A survey on graphic processing unit computing for large‐scale data mining

Cano

2017

WIREs Data Min & Knowl

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General purpose computation using Graphic Processing Units (GPUs) is a wellestablished research area focusing on high-performance computing solutions for massively parallelizable and time-consuming problems. Classical methodologies in machine learning and data mining cannot handle processing of massive and high-speed volumes of information in the context of the big data era. GPUs have successfully improved the scalability of data mining algorithms to address significantly larger dataset sizes in many application areas. The popularization of distributed computing frameworks for big data mining opens up new opportunities for transformative solutions combining GPUs and distributed frameworks. This survey analyzes current trends in the use of GPU computing for large-scale data mining, discusses GPU architecture advantages for handling volume and velocity of data, identifies limitation factors hampering the scalability of the problems, and discusses open issues and future directions.

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Density Based Clustering: Alternatives to DBSCAN

Cited by 18 publications

References 21 publications

Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository

Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository

A Short Review of Security-Aware Techniques in Real-Time Embedded Systems

A survey on graphic processing unit computing for large‐scale data mining

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