کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4943725 | 1437639 | 2017 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An evolutionary algorithm for clustering data streams with a variable number of clusters
ترجمه فارسی عنوان
یک الگوریتم تکاملی برای خوشه بندی جریان داده ها با تعداد متغیر خوشه ای
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کلمات کلیدی
الگوریتمهای تکاملی، خوشه بندی جریان داده ها، مفهوم رانش
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Several algorithms for clustering data streams based on k-Means have been proposed in the literature. However, most of them assume that the number of clusters, k, is known a priori by the user and can be kept fixed throughout the data analysis process. Besides the difficulty in choosing k, data stream clustering imposes several challenges to be addressed, such as addressing non-stationary, unbounded data that arrive in an online fashion. In this paper, we propose a Fast Evolutionary Algorithm for Clustering data streams (FEAC-Stream) that allows estimating k automatically from data in an online fashion. FEAC-Stream uses the Page-Hinkley Test to detect eventual degradation in the quality of the induced clusters, thereby triggering an evolutionary algorithm that re-estimates k accordingly. FEAC-Stream relies on the assumption that clusters of (partially unknown) data can provide useful information about the dynamics of the data stream. We illustrate the potential of FEAC-Stream in a set of experiments using both synthetic and real-world data streams, comparing it to four related algorithms, namely: CluStream-OMRk, CluStream-BkM, StreamKM++-OMRk and StreamKM++-BkM. The obtained results show that FEAC-Stream provides good data partitions and that it can detect, and accordingly react to, data changes.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 67, January 2017, Pages 228-238
Journal: Expert Systems with Applications - Volume 67, January 2017, Pages 228-238
نویسندگان
Jonathan de Andrade Silva, Eduardo Raul Hruschka, João Gama,