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首页> 外文期刊>Technological forecasting and social change >Multi-attribute large-scale group decision making with data mining and subgroup leaders: An application to the development of the circular economy
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Multi-attribute large-scale group decision making with data mining and subgroup leaders: An application to the development of the circular economy

机译：多属性大规模集团决策与数据挖掘和子群体领导者：循环经济发展的应用

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摘要

The circular economy is a concept that emphasizes a sustainable and regenerative method of business operations. The circular economy has become the economic embodiment and inevitable choice for the implementation of sustainable development strategies. For many circular economy activities such as the selection of pilot parks or cities, many experts from multiple fields or ministries are often invited to make decisions according to multiple attributes. Hence, to solve such problems, it is necessary to develop an efficient multiattribute large-scale group decision-making model that can facilitate coordination of a large group of experts. First, a natural language processing technique from a specific data mining application field is adopted to mine public preference information. Then, experts are clustered and subgroup leaders are selected. Next, a consensus reaching model is proposed to reduce the discrepancies among experts. Finally, an illustrative example regarding the selection of pilot eco-industrial parks in the Sichuan Province, China, is given to demonstrate the applicability of the proposed model. The results show that our model can effectively address evaluation problems of circular economy activities involving a large group of experts.
机译：循环经济是一种强调可持续和再生业务方法的概念。循环经济已成为实施可持续发展战略的经济体现和必然选择。对于许多循环经济活动，例如选择飞行员公园或城市，许多来自多个领域或部委的许多专家通常被邀请根据多个属性做出决定。因此，为了解决这些问题，有必要开发一种有效的多元大规模集团决策模型，可以促进一大群专家的协调。首先，采用来自特定数据挖掘应用领域的自然语言处理技术对挖掘公共偏好信息。然后，专家是集群，选择子组领导者。接下来，建议达成共识达成模型，以减少专家之间的差异。最后，在中国四川省飞行员生态工业园区选择的一个说明性示例，展示了拟议模型的适用性。结果表明，我们的模型可以有效地解决了涉及一大群专家的循环经济活动的评价问题。

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