利用广义细胞自动机实现的智能数据聚类

现有的数据聚类方法仍存在着各种不足，聚类速度和结果的质量不能满足大型、高维数据库上的聚类需求。本文提出了一种新的基于广义细胞自动机的数据聚类算法，利用细胞自动机的自组织能力对数据进行聚类分析。聚类结果的质量不受聚类大小和聚类形状的影响，可以通过随机抽样应用于大数据集。文章在细胞结构及细胞动力学规则中引入了细胞核的概念，让细胞自动机利用自身的演化找出数据中的聚类信息。文章通过分析证明了本文方法的有效性，并通过模拟软件对算法性能进行了详细的实验，证明了算法的实用性和高效性。

Data Clustering Algorithm Based On Swarm Intelligence and Generalized Cellular Automata

A variety of clustering approaches have been proposed so far, nevertheless they are not qualified to quickly cluster a large-scale high-dimensional database. This paper is devoted to a novel data clustering approach based on generalized cellular automata (GCA). The GCA extends the traditional cellular automata in the sense of cellular structure and its dynamics, so that the evolution of GCA eventually results in data clustering. The proposed approach is featured by the self-organizing clustering and many advantages in terms of the insensitivity to noise, quality robustness to clustered data, suitability for high-dimensional and massive data set. The analysis, comparison and simulations have shown the effectiveness and excellent performance of our GCA approach to data clustering.