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Data Mining of ACO-Based Rough Sets and Application in Construction Projects Cost Analysis

机译:基于ACO的粗糙集数据挖掘和建设项目成本分析的应用

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In this paper, the reduction algorithm based on rough sets (RS) is proposed as a practical data mining technology. It has been proven that the information system reduction is a NP-hard problem. NP-hard problem is a major property portfolio explosions. Thus, the only solution to this problem is the development of heuristic search method. Ant colony optimization, which was introduced in the early 1990s as a novel technique for solving hard combinatorial optimization problems, finds itself currently at this point of its life cycle.With this article we implying the use of ant colony optimization (ACO) algorithm for resolving the NP-hard problem in rough set attribute reduction. Using ACO-based rough sets, construction projects cost was analyzed and the results show that this method is more convenient and practical compared with the traditional one.
机译:本文提出了一种基于粗糙集(RS)的减少算法作为实际数据挖掘技术。已证明信息系统减少是一个难题的问题。 NP-Coll问题是一个主要的财产投资组合爆炸。因此,对此问题的唯一解决方案是启发式搜索方法的发展。蚂蚁殖民地优化在20世纪90年代初作为解决硬组合优化问题的新技术,目前在其生命周期中发现本身。这篇文章暗示了蚁群优化(ACO)算法来解析粗糙集属性降低中的NP难题。使用基于ACO的粗糙集,分析了建设项目成本,结果表明,与传统的方法相比,这种方法更方便和实用。

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