摘要
量子博弈是对经典博弈的量子模拟,利用量子的纠缠态,可以使博弈参与人在博弈策略的选择过程中相互影响,从而得到与经典博弈不同的结果。将量子博弈运用于聚类问题,并提出一种基于网格的量子博弈聚类算法。算法将数据点看作是博弈的参与人,通过在收益矩阵中内嵌距离函数,使相似的数据点能够获得更大的收益,从而形成聚类。此外,通过设定网格合并规则,使博弈过程得到了简化。仿真实验表明,算法在聚类质量上优于传统的K-means等算法。最后,就算法中的几个参数对算法性能的影响进行了讨论,并给出了参数选择的建议。
Quantum game is an analogy of classical game. Using each other implicitly, and the game will result in a different way. the quantum entanglement, game players interact with Quantum game was applied to clustering. A clustering algorithm based on quantum game and grid was proposed where data points are regarded as players. By embeding distance function into payoff matrix, similar data points can get more payoff, and clusters will be formed in that way. In addition, a rule about merging grid was designed to simplify the game. Simulations show the clustering quality of this algorithm is superior to K-means etc. At last, several parameters in this algorithm were discussed and some recommendations about parameters selection were provided.
出处
《计算机科学》
CSCD
北大核心
2014年第10期261-265,共5页
Computer Science
关键词
博弈论
量子博弈
网格
聚类
Game theory, Quantum game, Grid, Clustering
