可拓数据挖掘方法及其应用研究
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摘要
随着计算机技术的迅速发展以及数据库管理系统的广泛应用,使得人们积累的数据远远超过人们分析和理解数据的能力。激增的数据背后隐藏着许多重要的信息,人们希望能够对其进行更深层次的分析,以便更好地应用数据和提供决策支持。数据挖掘技术为了解决“数据爆炸但知识贫乏”的问题便应运而生。成为目前具有挑战意义的研究热点之一。数据挖掘就是从大量的、不完全的、冗余的、有噪声的数据集中识别出有效的、新颖的、潜在有用的,以及最终可理解的信息和知识的过程。
随着经济全球化的推进,环境的多变促使了信息和知识的更新周期缩短,创新和解决矛盾问题越来越成为各行各业的重要工作。因此,如何挖掘变化的知识就成为数据挖掘研究的重要任务。可拓数据挖掘是可拓学和数据挖掘结合的产物,它探讨利用可拓学方法和数据挖掘技术,去挖掘数据库中与可拓变换有关的知识,包括可拓预测知识、可拓分类知识、可拓关联规则和传导知识等可拓知识。
在全面综述国内外现有可拓数据挖掘方法的基础上,从方法到应用对可拓数据挖掘关键技术进行了深入的研究,综合运用可拓学理论、粗糙集理论、模糊理论、集对分析等其它数据挖掘方法,寻找出一种行之有效的创新模型化方法,即可拓数据挖掘模型与方法,主要研究成果如下:
(1)基于可拓聚类的预测方法研究
传统的预测方法往往受样本数目的限制,而且对于指标的变化不能准确的、定量的描述,针对传统预测方法的复杂性,结合可拓学和聚类方法,建立可拓聚类预测模型,首先通过系统聚类的方法,对相近的属性聚类,以达到属性约简,然后对余下的各属性变化率进行可拓聚类,采用比重权数法和主观经验来确定权重系数,来进行可拓聚类预测。最后以中国联通2002-2008年的相关指标,来预测企业另外某个指标的量值。对2008年的某个指标进行可拓聚类预测,预测结果和中国联通2008年公布的年报中某个指标的数值是完全相符的,说明利用可拓聚类预测方法进行预测是可行的。其分析结果对于中国联通战略的制定有一定的参考意义。
(2)基于可拓的客户繁衍价值研究与应用
当前客户价值评价侧重于静态的描述,缺乏动态的研究,特别是基于口碑效应的潜在价值的定量研究,针对当前客户价值评价的缺陷和难以描述定量定性相结合的不足,结合可拓学和客户价值理论,用共轭分析方法,细分客户价值,提出了基于可拓的客户繁衍价值,通过定性和定量的方法,建立基于可拓的客户繁衍价值模型。最后针对中国联通近期推出的推荐有奖,入网有礼活动,定量计算客户的繁衍价值,其分析结果对于中国联通销售策略的制定具有一定的参考意义,对其他企业的营销策略、口碑和形象宣传也具有一定的参考意义。
(3)基于粗糙集的可拓数据挖掘及其在企业品牌细分中的应用
针对当前数据挖掘属性约简和权值系数确定的复杂性和主观性,结合可拓学和粗糙集方法,建立企业品牌细分模型,首先通过粗糙集联系度的方法,对属性进行约简;再利用粗糙集方法和相关经验确定各属性的权值系数,对企业品牌进行细分。最后通过对中国联通现有的三大品牌相关指标分析,并提出品牌整合建议,能对新客户的入网提供适宜的差异化服务。其研究结果对企业的客户关系管理具有一定的工程实践意义。
(4)基于可拓的关联规则研究及其应用
关联规则的有效性是随时间发生动态改变的,针对当前关联规则挖掘的静态性,结合可拓学和关联规则方法,首先分析可拓变化引起的正质变域、负质变域、正量变域、负量变域和拓界,对关联规则的前后件分别进行正可拓变换、负可拓变换、正稳定变换、负稳定变换和拓界变换,然后给出可能的可拓关联规则,着力分析正可拓变换情况和负可拓变换情况,并给出相应的支持度和可信度。最后对中国联通新套餐推出前后的指标变化进行分析,着重分析其正负可拓变换,针对不同类型的客户,给出可行的套餐建议。结果说明基于可拓的关联规则是有效的。其分析结果对于中国联通销售策略的制定具有一定的工程实践意义。
最后进行了概括性总结,并提出了有待进一步研究的方向。研究成果对于中国联通乃至其他企业的战略决策、客户关系管理、营销策略、形象宣传具有一定的理论意义和工程实践意义。
With rapid development of computer technologies and increasing applications of database management systems, the accumulated data in modem life are far beyond our normal capabilities in analyzing and understanding them without the use of automated analysis technologies. In fact, important information may be found behind the accumulated data. Therefore, it is necessary to systematically and deeply analyze these data, which provide clues for scientific decisions. Data mining has been developed to solve the above challenges. Data mining, an information extraction process, can be used to explore hidden facts out of the database. Out of question, data mining has become one of the most heated topics in the field of information technology.
With the advancement of global economics, the renewal period for the information and knowledge has been shortened because of the rapid environmental change. Innovations and solutions of problems become more and more important in various fields. Therefore, how to mine extension knowledge becomes critical for the research of data mining. The research indicated that extension data mining shows promising applications in many fields. Extension data mining is a combination of extenics and data mining. It is used to explore the knowledge about extension transformations in databases, i.e., extension knowledge which takes advantage of extension methods and data mining technologies such as extension classification knowledge, extension clustering prediction knowledge, extension association rules knowledge, and conductive knowledge.
After reviewing and studying the available approaches on data mining reported, this thesis discovers an effective and creative model, i.e., Extension Data Mining model, with a combination of extenics, fuzzy theory, rough set theory, sets pair analysis, and other data mining technology. The main results of the thesis are summarized as below:
(1) Research on extension clustering prediction
Based on the complexity of traditional forecasting methods, an extension clustering prediction model has been built up by combing extenics and clustering method. Firstly, attribute reduction was achieved for clustering with similar attributes through the hierarchical clustering method; secondly, the extension clustering for the remaining attributes were made by the use of their changing rates, and weight coefficient was determined with a simple correlation function; finally, the results show that it is available to predict indicators of China Unicom by using extension clustering prediction method. Therefore, this method may be beneficial for making decisions and expanding markets.
(2) Research on customer propagation values based on extension methods
Faced with the shortcomings of current customers' evaluation and thedeficiencies of combined quantitative and qualitative descriptions, this thesis has subdivided customers' values with the use of conjugation analysis combined with extension and customer value theory, and the thesis also provides customer propagation values. The customer propagation value model has been built up through both qualitative and quantitative methods. The results present beneficial references to make marketing strategies and promotional images.
(3) Research on extension data mining in subdivisions of telecommunication enterprise brands.
Faced with the complexity and subjectivity of the current attribution reduction as well as the determination of weight coefficient, sub-brand model has been created with combination of extenics and rough set methods. There are some attributes which are unimportant to the decision attribute, and some records that disturb on making decisions. Reducing the condition attributes based on the matter-element theory and rough sets pair analysis, the thesis has calculated the importance of the decision attribute for each condition attribute after reduction. The thesis has also determined weight coefficient through the use of rough set methods and relevant experience. This work may be beneficial on how to integrate the existing brands and how to recommend an appropriate brand to new customers. Through the analysis of three indicators of brands of China Unicom, the results show that extension data mining can provide effective support for the Decision-making of enterprise and appropriate service differentiation.
(4) Research on extension association rules
In connection with static characteristics of current association rules and combination with extension methods and association rules, we have firstly analyzed the positive extension field, the negative extension field, the positive stable field, the negative stable field, and the extension boundary owing to extension transformation. And then we analyze the change association rules and various situations of the changes of conditions and conclusions, and_present support and confidence. The result shows it is available to analyze China Unicorn's package by using extension association rules. Research results provide beneficial references to marketing strategies.
Finally, a brief summary and some future research directions are highlighted in this dissertation. Research results provide beneficial references to decision-making, customer relationship management, marketing strategy and promotional image. This thesis is very important and beneficial to both theoretical research and engineering practice.
随着经济全球化的推进,环境的多变促使了信息和知识的更新周期缩短,创新和解决矛盾问题越来越成为各行各业的重要工作。因此,如何挖掘变化的知识就成为数据挖掘研究的重要任务。可拓数据挖掘是可拓学和数据挖掘结合的产物,它探讨利用可拓学方法和数据挖掘技术,去挖掘数据库中与可拓变换有关的知识,包括可拓预测知识、可拓分类知识、可拓关联规则和传导知识等可拓知识。
在全面综述国内外现有可拓数据挖掘方法的基础上,从方法到应用对可拓数据挖掘关键技术进行了深入的研究,综合运用可拓学理论、粗糙集理论、模糊理论、集对分析等其它数据挖掘方法,寻找出一种行之有效的创新模型化方法,即可拓数据挖掘模型与方法,主要研究成果如下:
(1)基于可拓聚类的预测方法研究
传统的预测方法往往受样本数目的限制,而且对于指标的变化不能准确的、定量的描述,针对传统预测方法的复杂性,结合可拓学和聚类方法,建立可拓聚类预测模型,首先通过系统聚类的方法,对相近的属性聚类,以达到属性约简,然后对余下的各属性变化率进行可拓聚类,采用比重权数法和主观经验来确定权重系数,来进行可拓聚类预测。最后以中国联通2002-2008年的相关指标,来预测企业另外某个指标的量值。对2008年的某个指标进行可拓聚类预测,预测结果和中国联通2008年公布的年报中某个指标的数值是完全相符的,说明利用可拓聚类预测方法进行预测是可行的。其分析结果对于中国联通战略的制定有一定的参考意义。
(2)基于可拓的客户繁衍价值研究与应用
当前客户价值评价侧重于静态的描述,缺乏动态的研究,特别是基于口碑效应的潜在价值的定量研究,针对当前客户价值评价的缺陷和难以描述定量定性相结合的不足,结合可拓学和客户价值理论,用共轭分析方法,细分客户价值,提出了基于可拓的客户繁衍价值,通过定性和定量的方法,建立基于可拓的客户繁衍价值模型。最后针对中国联通近期推出的推荐有奖,入网有礼活动,定量计算客户的繁衍价值,其分析结果对于中国联通销售策略的制定具有一定的参考意义,对其他企业的营销策略、口碑和形象宣传也具有一定的参考意义。
(3)基于粗糙集的可拓数据挖掘及其在企业品牌细分中的应用
针对当前数据挖掘属性约简和权值系数确定的复杂性和主观性,结合可拓学和粗糙集方法,建立企业品牌细分模型,首先通过粗糙集联系度的方法,对属性进行约简;再利用粗糙集方法和相关经验确定各属性的权值系数,对企业品牌进行细分。最后通过对中国联通现有的三大品牌相关指标分析,并提出品牌整合建议,能对新客户的入网提供适宜的差异化服务。其研究结果对企业的客户关系管理具有一定的工程实践意义。
(4)基于可拓的关联规则研究及其应用
关联规则的有效性是随时间发生动态改变的,针对当前关联规则挖掘的静态性,结合可拓学和关联规则方法,首先分析可拓变化引起的正质变域、负质变域、正量变域、负量变域和拓界,对关联规则的前后件分别进行正可拓变换、负可拓变换、正稳定变换、负稳定变换和拓界变换,然后给出可能的可拓关联规则,着力分析正可拓变换情况和负可拓变换情况,并给出相应的支持度和可信度。最后对中国联通新套餐推出前后的指标变化进行分析,着重分析其正负可拓变换,针对不同类型的客户,给出可行的套餐建议。结果说明基于可拓的关联规则是有效的。其分析结果对于中国联通销售策略的制定具有一定的工程实践意义。
最后进行了概括性总结,并提出了有待进一步研究的方向。研究成果对于中国联通乃至其他企业的战略决策、客户关系管理、营销策略、形象宣传具有一定的理论意义和工程实践意义。
With rapid development of computer technologies and increasing applications of database management systems, the accumulated data in modem life are far beyond our normal capabilities in analyzing and understanding them without the use of automated analysis technologies. In fact, important information may be found behind the accumulated data. Therefore, it is necessary to systematically and deeply analyze these data, which provide clues for scientific decisions. Data mining has been developed to solve the above challenges. Data mining, an information extraction process, can be used to explore hidden facts out of the database. Out of question, data mining has become one of the most heated topics in the field of information technology.
With the advancement of global economics, the renewal period for the information and knowledge has been shortened because of the rapid environmental change. Innovations and solutions of problems become more and more important in various fields. Therefore, how to mine extension knowledge becomes critical for the research of data mining. The research indicated that extension data mining shows promising applications in many fields. Extension data mining is a combination of extenics and data mining. It is used to explore the knowledge about extension transformations in databases, i.e., extension knowledge which takes advantage of extension methods and data mining technologies such as extension classification knowledge, extension clustering prediction knowledge, extension association rules knowledge, and conductive knowledge.
After reviewing and studying the available approaches on data mining reported, this thesis discovers an effective and creative model, i.e., Extension Data Mining model, with a combination of extenics, fuzzy theory, rough set theory, sets pair analysis, and other data mining technology. The main results of the thesis are summarized as below:
(1) Research on extension clustering prediction
Based on the complexity of traditional forecasting methods, an extension clustering prediction model has been built up by combing extenics and clustering method. Firstly, attribute reduction was achieved for clustering with similar attributes through the hierarchical clustering method; secondly, the extension clustering for the remaining attributes were made by the use of their changing rates, and weight coefficient was determined with a simple correlation function; finally, the results show that it is available to predict indicators of China Unicom by using extension clustering prediction method. Therefore, this method may be beneficial for making decisions and expanding markets.
(2) Research on customer propagation values based on extension methods
Faced with the shortcomings of current customers' evaluation and thedeficiencies of combined quantitative and qualitative descriptions, this thesis has subdivided customers' values with the use of conjugation analysis combined with extension and customer value theory, and the thesis also provides customer propagation values. The customer propagation value model has been built up through both qualitative and quantitative methods. The results present beneficial references to make marketing strategies and promotional images.
(3) Research on extension data mining in subdivisions of telecommunication enterprise brands.
Faced with the complexity and subjectivity of the current attribution reduction as well as the determination of weight coefficient, sub-brand model has been created with combination of extenics and rough set methods. There are some attributes which are unimportant to the decision attribute, and some records that disturb on making decisions. Reducing the condition attributes based on the matter-element theory and rough sets pair analysis, the thesis has calculated the importance of the decision attribute for each condition attribute after reduction. The thesis has also determined weight coefficient through the use of rough set methods and relevant experience. This work may be beneficial on how to integrate the existing brands and how to recommend an appropriate brand to new customers. Through the analysis of three indicators of brands of China Unicom, the results show that extension data mining can provide effective support for the Decision-making of enterprise and appropriate service differentiation.
(4) Research on extension association rules
In connection with static characteristics of current association rules and combination with extension methods and association rules, we have firstly analyzed the positive extension field, the negative extension field, the positive stable field, the negative stable field, and the extension boundary owing to extension transformation. And then we analyze the change association rules and various situations of the changes of conditions and conclusions, and_present support and confidence. The result shows it is available to analyze China Unicorn's package by using extension association rules. Research results provide beneficial references to marketing strategies.
Finally, a brief summary and some future research directions are highlighted in this dissertation. Research results provide beneficial references to decision-making, customer relationship management, marketing strategy and promotional image. This thesis is very important and beneficial to both theoretical research and engineering practice.
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