层次聚类
聚类分析
计算机科学
网络的层次聚类
启发式
棕色聚类
单元制造
星团(航天器)
抓住
数据挖掘
数学优化
相关聚类
人工智能
树冠聚类算法
数学
程序设计语言
操作系统
作者
Samiran Bera,Manojit Chattopadhyay,Pranab K. Dan
标识
DOI:10.1177/0954405417699014
摘要
Cellular manufacturing is a prevailing pursuit in increasing productivity and throughput and in reducing setup and lead time in production systems that are configured as clusters of machines or facilities. Although several approaches exist today to optimize the quality of cluster, they are cumbersome in terms of implementation due to high complexity and resource requirements. In this work, these issues are addressed effectively using a novel two-stage approach which integrates centre ordering of vectors on ring and an agglomerative hierarchical clustering. The centre ordering of vectors on ring–agglomerative hierarchical clustering approach is much simpler and quicker to grasp and implement in a resource-constraint environment. Problem sets retrieved from the existing literature are put to experimentation for optimality and are compared to study improvements using grouping efficacy. The proposed method has yielded significant improvement, as found from the experimental results, over earlier reported ones. This establishes the centre ordering of vectors on ring–agglomerative hierarchical clustering algorithm as an efficient approach for clustering. Furthermore, the number of iterations required to reach optimality is significantly lesser, compared to the ones obtained through existing approaches based on mathematical models and heuristics.
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