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Efficient Utilization of DBMS Potential in Spatial Data Mining Applications - Neighborhood Relation Modeling Approach

机译:DBMS潜力在空间数据挖掘应用中的有效利用-邻域关系建模方法

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In the core concept of spatial data mining we need to investigate the neighbors of many objects in the single run of typical data mining algorithm. This means that in spatial data mining algorithm we have to efficiently process the neighborhood relations. An integration of spatial data mining algorithms and the potential of spatial database management system (SDBMS) will help efficiently providing general concept of neighborhood relation and its implementation. If properly integrated, this will speed up the industrial application development of spatial database and applying the traditional data mining concept on that[1][4][5][6]. Here in this paper the neighborhood relation and neighbourhood index has been discussed and the related small set of database primitives has been defined. The proposed basic operations reduce the search space and support well the typical data mining algorithms. Such database primitives have been implemented on top of a commercially available Spatial Database Management System (SDBMS) Oracle10g [6][7].
机译:在空间数据挖掘的核心概念中,我们需要在典型数据挖掘算法的单次运行中研究许多对象的邻居。这意味着在空间数据挖掘算法中,我们必须有效地处理邻域关系。空间数据挖掘算法与空间数据库管理系统(SDBMS)的潜力的集成将有助于有效地提供邻域关系及其实现的一般概念。如果适当地集成,这将加速空间数据库的工业应用开发,并在[1] [4] [5] [6]上应用传统的数据挖掘概念。在本文中,讨论了邻域关系和邻域索引,并定义了相关的数据库原语的小型集合。提出的基本操作减少了搜索空间并很好地支持了典型的数据挖掘算法。这样的数据库原语已经在商业上可用的空间数据库管理系统(SDBMS)Oracle10g [6] [7]之上实现。

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