کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
385859 660873 2011 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Scalable local density-based distributed clustering
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Scalable local density-based distributed clustering
چکیده انگلیسی

Large amounts of high-dimensional data are distributed with the application of networks. Distributed clustering has become an increasingly important task due to variety of real-life constrains, including bandwidth and security aspects. Many distributed clustering algorithm have been proposed, but most of them have high transmission cost and poor clustering quality. In this paper, we propose a scalable local density-based distributed clustering algorithm which can easily fit high-dimensional data sets by this method such as density attractor distance and noise factor. In order to keep a lower transmission cost, we determine suitably low factor noises to send to the server. Furthermore, Test data sets, CMC data sets and KDD-CUP-99 are used for experimental evaluation to validate the performance practically. The experimental results and theoretical analysis show that the efficiency and quality for clustering of the proposed algorithm are superior to the other distributed clustering algorithm.

Research highlights
► We propose a scalable local density-based distributed clustering which can easily fit high-dimensional data sets.
► We determine suitably low factor noises to send to the server in order to keep a lower transmission cost.
► Test data sets, CMC data sets and KDD-CUP-99 are used for experimental evaluation to validate our algorithm performance practically.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 38, Issue 8, August 2011, Pages 9491–9498
نویسندگان
, ,