با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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A dynamic classification unit for online segmentation of big data via small data buffers
واحد طبقه بندی پویا برای تقسیم آنلاین داده های بزرگ از طریق بافر داده های کوچک-2020 In many segmentation processes, we assign new cases according to a model that was built on the basis of past
cases. As long as the new cases are “similar enough” to the past cases, segmentation proceeds normally.
However, when a new case is substantially different from the known cases, a reexamination of the previously
created segments is required. The reexamination may result in the creation of new segments or in the updating of
the existing ones. In this paper, we assume that in big and dynamic data environments it is not possible to
reexamine all past data and, therefore, we suggest using small groups of selected cases, stored in small data
buffers, as an alternative to the collection of all past data. We present an incremental dynamic classifier that
supports real-time unsupervised segmentation in big and dynamic data environments. In order to reduce the
computational effort of unsupervised clustering in such environments, the suggested model performs calculations
only on the relevant data buffers that store the relevant representative cases. In addition, the suggested
model can serve as a dynamic classification unit (DCU) that can act as an autonomous agent, as well as collaborate
with other DCUs. The evaluation is presented by comparing three approaches: static, dynamic, and incremental
dynamic. Keywords: Incremental dynamic classifier | Dynamic segmentation | Incremental data analysis | Cluster analysis | Classification | Big data |
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