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首页> 外文期刊>Arabian Journal for Science and Engineering >Comparative Performance Evaluation of Clustering Algorithms for Grouping Manufacturing Firms
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Comparative Performance Evaluation of Clustering Algorithms for Grouping Manufacturing Firms

机译：分组制造企业聚类算法的比较绩效评估

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摘要

Surviving and remaining profitable in market is the biggest requirement for any enterprise in today’s business environment because of the stiff competition. Being innovative and staying updated with the latest and trending changes happening in the sector is the key for firms to leave their impression as successful and profit building business house. However, it is not necessary that all the firms should follow same pattern of structure or production procedure even if they come under the same cap of industry but at some point of time up gradation is needed. Appropriate grouping of various manufacturing firms plays a vital role in assessing their credentials. Data relating to appropriate attributes of three types of manufacturing firms are collected. By employing five different clustering techniques each type of these firms are grouped and rank of each method and for each category of industry is evaluated and presented. Results obtained from analysis demonstrate that the overall ranking based on cluster potentiality of different methods is ordered as Self Organized Maps, Gaussian Mixture Model, Fuzzy C-Means, K-Means and Hierarchical techniques. Finding of this study can help the decision makers to devise appropriate strategy for their production pattern according to the firm capability.
机译：在当今的商业环境中，由于竞争激烈，生存和保持盈利是任何企业的最大要求。保持行业的最新和趋势变化的创新能力，这是公司离开自己的成功和盈利能力的关键。但是，即使所有公司都属于同一行业上限，也没有必要所有公司都遵循相同的结构或生产程序模式，但在某个时间点需要向上分级。各种制造公司的适当分组在评估其资质方面起着至关重要的作用。收集与三种类型的制造公司的适当属性有关的数据。通过采用五种不同的聚类技术，对这些公司的每种类型进行分组，并对每种方法进行排名，并对每种行业进行评估和展示。分析获得的结果表明，基于不同方法的簇势的总体排名是按照自组织图，高斯混合模型，模糊C均值，K均值和层次技术排序的。这项研究的发现可以帮助决策者根据企业的能力为其生产模式设计适当的策略。

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