本文選題：數(shù)據(jù)挖掘 + 關(guān)聯(lián)規(guī)則��； 參考：《鄭州大學(xué)》2013年碩士論文

【摘要】：如何將數(shù)據(jù)倉(cāng)庫(kù)及數(shù)據(jù)挖掘的相關(guān)技術(shù)運(yùn)用到銀行金融產(chǎn)品的規(guī)劃與銷售中,是目前我國(guó)金融行業(yè)較為迫切需要研究的領(lǐng)域。該領(lǐng)域研究的內(nèi)容包括數(shù)據(jù)挖掘技術(shù)的研究、更加有效的挖掘算法設(shè)計(jì)、客戶關(guān)系管理系統(tǒng)的重新構(gòu)建等方面。本文具體探討了銀行理財(cái)產(chǎn)品銷售分析系統(tǒng)在實(shí)施過(guò)程中的若干關(guān)鍵技術(shù),同時(shí)提出了一種有效挖掘負(fù)關(guān)聯(lián)規(guī)則的方法。 銀行客戶的眾多行為中,存在著正、負(fù)關(guān)聯(lián)規(guī)則。傳統(tǒng)的關(guān)聯(lián)規(guī)則算法僅反應(yīng)了正項(xiàng)之間的關(guān)聯(lián)關(guān)系,無(wú)法解決負(fù)關(guān)聯(lián)的問(wèn)題。本文提出了一種有效的算法(GA_PNAR),用以解決銀行客戶行為負(fù)關(guān)聯(lián)的問(wèn)題。GA_PNAR算法首先利用Apriori算法生成頻繁項(xiàng)集,之后利用基于相關(guān)系數(shù)的NRGA算法生成含有所有負(fù)項(xiàng)的關(guān)聯(lián)規(guī)則,在所有規(guī)則生成后,利用遺傳算法優(yōu)選生成的規(guī)則。GA_PNAR算法是一款非常有前途的發(fā)現(xiàn)規(guī)則的方法。 當(dāng)前針對(duì)銀行營(yíng)銷的方法主要考慮的是客戶的基本屬性,沒有全面考慮客戶的價(jià)值屬性以及行為屬性。營(yíng)銷方案的設(shè)計(jì)也主要是對(duì)客戶進(jìn)行細(xì)分,通過(guò)對(duì)客戶的基本屬性如投資期限、風(fēng)險(xiǎn)性偏好等進(jìn)行聚類分析。根據(jù)聚類分析的結(jié)果,將客戶的聚類特征與理財(cái)產(chǎn)品的特征結(jié)合,為客戶提供理財(cái)方案。在這種方案中客戶的投資期限、風(fēng)險(xiǎn)性偏好等屬性往往通過(guò)測(cè)試獲得,存在很大的不準(zhǔn)確性和失真。本文將GA_PNAR方法應(yīng)用于銀行客戶-產(chǎn)品之間關(guān)聯(lián)規(guī)則的發(fā)現(xiàn),該過(guò)程選取的客戶數(shù)據(jù)主要是客戶行為屬性。相比常規(guī)的聚類分析,客戶-理財(cái)產(chǎn)品關(guān)聯(lián)規(guī)則分析,能夠?yàn)殂y行客戶提供更加精確、專業(yè)化的理財(cái)產(chǎn)品指導(dǎo)。另外,在該模型的數(shù)據(jù)預(yù)處理階段采用了云模型的方法對(duì)數(shù)值型屬性進(jìn)行了概念化分層,該方法可以有效地解決數(shù)值分層的模糊性問(wèn)題。最后,本文提出了一個(gè)銀行理財(cái)產(chǎn)品營(yíng)銷系統(tǒng)的設(shè)計(jì)方案。 論文對(duì)于國(guó)內(nèi)金融行業(yè)實(shí)施結(jié)構(gòu)化數(shù)據(jù)挖掘技術(shù)、部署企業(yè)級(jí)數(shù)據(jù)倉(cāng)庫(kù)、完善客戶分類、加強(qiáng)客戶關(guān)系管理、市場(chǎng)銷售分析、金融產(chǎn)品規(guī)劃、市場(chǎng)需求動(dòng)態(tài)分析等各個(gè)方面均有一定的借鑒和現(xiàn)實(shí)指導(dǎo)意義。
[Abstract]:How to apply the related technology of data warehouse and data mining to the planning and sale of bank financial products is the most urgent need to be studied in our financial industry. The research in this field includes the research of data mining technology, the more effective mining algorithm, the re construction of the customer relationship management system and so on. In this paper, some key technologies in the implementation process of bank financial products sales analysis system are discussed, and an effective method for mining negative association rules is proposed.
In many behavior of bank customers, there are positive and negative association rules. The traditional association rule algorithm only reflects the relationship between positive items and can not solve the problem of negative correlation. This paper proposes an effective algorithm (GA_PNAR) to solve the negative association of bank customer behavior. The.GA_PNAR algorithm is first generated by the Apriori algorithm. Frequent itemsets, then use the NRGA algorithm based on correlation coefficients to generate association rules containing all negative items. After all rules are generated, the rule.GA_PNAR algorithm generated by genetic algorithm is a very promising method of discovering rules.
At present, the main consideration of the method of banking marketing is the basic attribute of the customer. It does not fully consider the value attribute and the behavior attribute of the customer. The design of the marketing plan is mainly to subdivide the customer, and through the clustering analysis of the basic attribute of the customer, such as the term of investment and the risk preference, etc., according to the result of the cluster analysis, The customer's clustering characteristics and the characteristics of financial products are combined to provide a financial plan for customers. In this scheme, the time limit of the customer's investment and the risk preference are often obtained by testing, and there is a lot of inaccuracy and distortion. This paper applies the GA_PNAR method to the discovery of Association rules between customers and products of the bank. Customer data are mainly customer behavior attributes. Compared to conventional clustering analysis, customer financial product association rules analysis can provide more accurate and professional financial product guidance for bank customers. In addition, a cloud model is used in the data preprocessing stage of the model to conceptualize the numerical attributes. The method can effectively solve the fuzziness of numerical layering. Finally, this paper proposes a design scheme for the marketing system of bank financial products.
This paper has a certain reference and practical significance for the domestic financial industry to implement structured data mining technology, deploy enterprise data warehouse, improve customer classification, strengthen customer relationship management, market sales analysis, financial product planning, market demand dynamic analysis and so on.

【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP311.13

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