On the Accuracy and Parallelism of GPGPU-Powered Incremental Clustering Algorithms
المؤلفون المشاركون
Chen, Chunlei
He, Li
Zhang, Huixiang
Zheng, Hao
Wang, Lei
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-10-11
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Incremental clustering algorithms play a vital role in various applications such as massive data analysis and real-time data processing.
Typical application scenarios of incremental clustering raise high demand on computing power of the hardware platform.
Parallel computing is a common solution to meet this demand.
Moreover, General Purpose Graphic Processing Unit (GPGPU) is a promising parallel computing device.
Nevertheless, the incremental clustering algorithm is facing a dilemma between clustering accuracy and parallelism when they are powered by GPGPU.
We formally analyzed the cause of this dilemma.
First, we formalized concepts relevant to incremental clustering like evolving granularity.
Second, we formally proved two theorems.
The first theorem proves the relation between clustering accuracy and evolving granularity.
Additionally, this theorem analyzes the upper and lower bounds of different-to-same mis-affiliation.
Fewer occurrences of such mis-affiliation mean higher accuracy.
The second theorem reveals the relation between parallelism and evolving granularity.
Smaller work-depth means superior parallelism.
Through the proofs, we conclude that accuracy of an incremental clustering algorithm is negatively related to evolving granularity while parallelism is positively related to the granularity.
Thus the contradictory relations cause the dilemma.
Finally, we validated the relations through a demo algorithm.
Experiment results verified theoretical conclusions.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Chen, Chunlei& He, Li& Zhang, Huixiang& Zheng, Hao& Wang, Lei. 2017. On the Accuracy and Parallelism of GPGPU-Powered Incremental Clustering Algorithms. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1139852
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Chen, Chunlei…[et al.]. On the Accuracy and Parallelism of GPGPU-Powered Incremental Clustering Algorithms. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1139852
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Chen, Chunlei& He, Li& Zhang, Huixiang& Zheng, Hao& Wang, Lei. On the Accuracy and Parallelism of GPGPU-Powered Incremental Clustering Algorithms. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1139852
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139852
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر