Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA; CAPES Foundation, Ministry of Education of Brazil, Brasília, DF 70040-020, Brazil.
Applied Computational Intelligence Laboratory, Department of Computer Science, Missouri University of Science and Technology, Rolla, MO 65409, USA.
Neural Netw. 2020 Jan;121:208-228. doi: 10.1016/j.neunet.2019.08.033. Epub 2019 Sep 9.
This paper presents a novel adaptive resonance theory (ART)-based modular architecture for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local fuzzy ART modules. It is equipped with distributed higher-order activation and match functions and a dual vigilance mechanism. Together, these allow DDVFA to perform unsupervised modularization, create multi-prototype cluster representations, retrieve arbitrarily-shaped clusters, and reduce category proliferation. Another important contribution is the reduction of order-dependence, an issue that affects any agglomerative clustering method. This paper demonstrates two approaches for mitigating order-dependence: pre-processing using visual assessment of cluster tendency (VAT) or post-processing using a novel Merge ART module. The former is suitable for batch processing, whereas the latter also works for online learning. Experimental results in online mode carried out on 30 benchmark data sets show that DDVFA cascaded with Merge ART statistically outperformed the best other ART-based systems when samples were randomly presented. Conversely, they were found to be statistically equivalent in offline mode when samples were pre-processed using VAT. Remarkably, performance comparisons to non-ART-based clustering algorithms show that DDVFA (which learns incrementally) was also statistically equivalent to the non-incremental (offline) methods of density-based spatial clustering of applications with noise (DBSCAN), single linkage hierarchical agglomerative clustering (SL-HAC), and k-means, while retaining the appealing properties of ART. Links to the source code and data are provided. Considering the algorithm's simplicity, online learning capability, and performance, it is an ideal choice for many agglomerative clustering applications.
本文提出了一种新的基于自适应共振理论（ART）的无监督学习模块化架构，即分布式对偶警戒模糊 ART（DDVFA）。DDVFA 由一个全局 ART 系统组成，其节点是局部模糊 ART 模块。它配备了分布式高阶激活和匹配函数以及对偶警戒机制。这些机制共同允许 DDVFA 执行无监督模块化、创建多原型聚类表示、检索任意形状的聚类，并减少类别扩散。另一个重要贡献是减少了阶依赖，这是影响任何聚合聚类方法的问题。本文展示了两种减轻阶依赖的方法：使用聚类趋势视觉评估（VAT）进行预处理或使用新的合并 ART 模块进行后处理。前者适用于批量处理，而后者也适用于在线学习。在 30 个基准数据集上进行的在线模式实验结果表明，当样本随机呈现时，与 Merge ART 级联的 DDVFA 在统计学上优于其他最佳的基于 ART 的系统。相反，当使用 VAT 对样本进行预处理时，在线模式下发现它们在统计学上等效。值得注意的是，与非 ART 聚类算法的性能比较表明，DDVFA（它是增量学习的）在统计学上与基于密度的具有噪声的应用程序空间聚类（DBSCAN）、单链接层次聚类（SL-HAC）和 k-均值的非增量（离线）方法等效，同时保留了 ART 的吸引人的特性。提供了与源代码和数据的链接。考虑到算法的简单性、在线学习能力和性能，它是许多聚合聚类应用的理想选择。
