MRG-DBSCAN: An Improved DBSCAN Clustering Method Based on Map Reduce and Grid
نویسندگان
چکیده
منابع مشابه
MRG-DBSCAN: An Improved DBSCAN Clustering Method Based on Map Reduce and Grid
DBSCAN is a density-based clustering algorithm. This algorithm clusters data of high density. The traditional DBSCAN clustering algorithm in finding the core object, will use this object as the center core, extends outwards continuously. At this point, the core objects growing, unprocessed objects are retained in memory, which will occupy a lot of memory and I/O overhead, algorithm efficiency i...
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This document describes the implementation of two density-based clustering algorithms: DBSCAN [Ester1996] and SNN [Ertoz2003]. These algorithms were implemented within the context of the LOCAL project [Local2005] as part of a task that aims to create models of the geographic space (Space Models) to be used in context-aware mobile systems. Here, the role of the clustering algorithms is to identi...
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This article describes the implementation and use of the R package dbscan, which provides complete and fast implementations of the popular density-based clustering algorithm DBSCAN and the augmented ordering algorithm OPTICS. Compared to other implementations, dbscan offers open-source implementations using C++ and advanced data structures like k-d trees to speed up computation. An important ad...
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In this work we propose an extension of the DBSCAN algorithm to generate clusters with fuzzy density characteristics. The original version of DBSCAN requires two parameters (minPts and ) to determine if a point lies in a dense area or not. Merging different dense areas results into clusters that fit the underlined dataset densities. In this approach, a single density threshold is employed for a...
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ژورنال
عنوان ژورنال: International Journal of Database Theory and Application
سال: 2015
ISSN: 2005-4270
DOI: 10.14257/ijdta.2015.8.2.12