Data mining and knowledge discovery are two important growing research fields in the last two decades due to the abundance of data collected from various sources. The exponentially growing volumes of generated data urge the development of several mining techniques to feed the needs for automatically derived knowledge. Clustering analysis (finding similar groups of data) is a well-established and widely used approach in data mining and knowledge discovery. In this paper, we introduce a clustering technique that uses game theory models to tackle multi-objective application problems. The main idea is to exploit a specific type of simultaneous move games, called congestion games. Congestion games offer numerous advantages ranging from being succinctly represented to possessing Nash equilibrium that is reachable in a polynomial-time. The proposed algorithm has three main steps: 1) it starts by identifying the initial players (or the cluster-heads), 2) it establishes the initial clusters' composition by constructing the game and try to find the equilibrium of the game. The third step consists of merging close clusters to obtain the final clusters. The experimental results show that the proposed clustering approach obtains good results and it is very promising in terms of scalability and performance.

中文翻译：

---

并行博弈的多目标聚类算法

由于从各种来源收集的大量数据，数据挖掘和知识发现是过去二十年中两个重要的增长研究领域。生成的数据量呈指数增长，促使开发多种挖掘技术来满足对自动派生知识的需求。聚类分析（寻找相似的数据组）是数据挖掘和知识发现中一种成熟且广泛使用的方法。在本文中，我们介绍了一种使用博弈论模型来解决多目标应用问题的聚类技术。主要思想是利用一种特定类型的同步移动博弈，称为拥塞博弈。拥塞博弈提供了许多优点，从简洁表示到拥有可在多项式时间内达到的纳什均衡。所提出的算法有三个主要步骤：1）它从识别初始参与者（或簇头）开始，2）它通过构建博弈来建立初始簇的组成并尝试找到博弈的均衡。第三步包括合并紧密的集群以获得最终的集群。实验结果表明，所提出的聚类方法获得了良好的结果，并且在可扩展性和性能方面非常有前途。