دانلود مقاله ISI انگلیسی شماره 79006
عنوان فارسی مقاله

PTS: الگوریتم خوشه بندی جریان توپولوژیکی طرح ریزی شده

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79006 2016 11 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
PTS: Projected Topological Stream clustering algorithm
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Neurocomputing, Volume 180, 5 March 2016, Pages 16–26

کلمات کلیدی
جریان داده ها؛ خوشه بندی داده؛ همسانی مداوم؛ تجزیه و تحلیل داده توپولوژیکی
پیش نمایش مقاله
پیش نمایش مقاله PTS: الگوریتم خوشه بندی جریان توپولوژیکی طرح ریزی شده

چکیده انگلیسی

High-dimensional data streams clustering is an attractive research topic, as there are several applications that generate a high number of attributes, bringing new challenges in terms of partitioning due to the curse of dimensionality. In addition, those applications produce unbounded sequences of data which cannot be stored for later analysis. Although the importance of this scenario, there are still very few algorithms available in the literature to meet this task. Despite the theoretical foundation of mathematical topology for dealing with high-dimensional spaces, none of those approaches have investigated the problem of finding topologically similar projected clusters in high-dimensional data streams. Among the advantages of topology is the possibility to analyze data in a coordinate-free and noise-robust manner. In a previous research, we have shown that topologically similar clusters can be meaningful considering real-world data sets. In this paper, we extend those ideas and propose PTS, an algorithm for finding topologically similar clusters in high-dimensional data streams. The algorithm is capable of finding traditional projected clusters and then merging them according to topological features computed using persistent homology. Experiments with synthetic data streams of dimensions d=8,16,32,64d=8,16,32,64 and 128 confirm the ability of PTS to find topologically similar projected clusters.

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