| 基于案例学习的多层次聚类指标客观权重极大熵挖掘模型 |
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| 引用本文： | 曹颖赛,刘思峰,方志耕,曾友春,王欢. 基于案例学习的多层次聚类指标客观权重极大熵挖掘模型[J]. 中国管理科学, 2019, 27(2): 197-204. DOI: 10.16381/j.cnki.issn1003-207x.2019.02.020 |
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| 作者姓名： | 曹颖赛 刘思峰 方志耕 曾友春 王欢 |
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| 作者单位： | 1. 南京航空航天大学经济与管理学院, 江苏 南京 210016;2. 陆军军事交通学院汽车士官学校运输指挥系, 安徽 蚌埠 233011 |
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| 基金项目： | 国家自然科学基金资助项目（71671091，71701098，71801127）；国家社科基金项目军事类项目（16GJ003-018）；江苏省自然科学基金资助项目（BK20160940） |
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| 摘 要： | 本文针对待聚类对象的多层次聚类指标权重配置问题进行了研究。首先运用向量空间模型将聚类对象表征为包含多个层次聚类属性指标的特征空间向量并基于余弦距离测算底层属性指标的相似程度，然后根据聚类指标的层次结构以及相应各层指标的权重系数综合测算对象之间的相似程度，最后根据历史聚类案例中相同类别对象之间相似度较大，不同类别对象之间相似度较小等特点，构建了基于案例学习的多层次聚类指标客观权重极大熵挖掘模型。通过案例分析以及与其他方法的比较研究，证明了本模型的可行性与有效性，为多层次聚类指标客观赋权问题提供了一种新的研究思路。
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| 关 键 词： | 案例学习 特征空间向量 极大熵 权重挖掘 |
| 收稿时间： | 2017-04-19 |
| 修稿时间： | 2017-12-28 |
An Objective Weight Maximum Entropy Mining Model for Multi-level Clustering Indexes Based on Case Learning |
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| CAO Ying-sai,LIU Si-feng,FANG Zhi-geng,ZENG You-chun,WANG Huan. An Objective Weight Maximum Entropy Mining Model for Multi-level Clustering Indexes Based on Case Learning[J]. Chinese Journal of Management Science, 2019, 27(2): 197-204. DOI: 10.16381/j.cnki.issn1003-207x.2019.02.020 |
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| Authors: | CAO Ying-sai LIU Si-feng FANG Zhi-geng ZENG You-chun WANG Huan |
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| Affiliation: | 1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. Institute of Transportation Command, Army Military Transportation University, Bengbu 233011, China |
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| Abstract: | The weight of characteristic attribute index is a significant influence factor during the process of multiple criteria clustering decision aids. Hence,many researches have focused on this important research area. Historical clustering information can effectively provide importance measures for each index with regard to clustering the objects which are to be evaluated. Learning of previous cases can not only contributes to the reveal of the objective law of clustering but also dig out the weight of each attribute index. However, this significant information has been overlooked by many previous researches which can definitely lead to the inaccurate weight calculation. Case learning, in this paper, is defined as the method proposed by the self-reasoning of the results of typical case sets and calculating some of the key parameters, so as to construct the proper decision-making models which can be applied to the evaluation of new objects in the future. To make the most of the existing clustering cases, the objects which are to be clustered as multidimensional attributes are defined by using space vector model. Based on the fact that objects in the same category are more similar than those in different categories, cosine distance is introduced to measure the similarity among different objects. Maximum entropy model is also employed to estimate the expected contribution of different indexes located in diverse levels to the category of the whole object. An illustrative example about weight allocation of attribute indexes in criminal cases is presented in this paper to show how the new approach is applied in the practical clustering decision problem. The feasibility and validity of the newly-proposed method is demonstrated through the comparison analysis with other similar methods. As a decision support, the proposed model can also provide a novel standpoint for weight calculation of objects with multi-level attribute indexes. |
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| Keywords: | case study feature space vector maximum entropy weight mining |
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