Evaluating discounts as a dimension of customer behavior analysis

Today, increased competition between organizations has led them to seek a better understanding of customer behavior through identifying valuable customers. Customers’ expectations about the price and quality of products and services play an important role in their selection process. In online businesses, competition and price differences between suppliers is high, so discounts will attract different customers. As a result, discounts and the frequency and amount of purchases can lead to better understanding of customer behavior. Customer segmentation and analysis is essential for identifying groups of customers. Hence, this study uses a model based on RFM called RdFdMd, in which d is the level of discount used to analyze customer purchase behavior and the importance of discounts on customers’ purchasing behavior and organizational profitability. The CRISP-DM and k-mean algorithm were used for clustering. The results indicate that using the R_dF_dM_d model achieves better customer clustering and valuation, and discounts were identified as an important criterion for customer purchases.     

Crowdsourcing-based semantic relation recognition for natural language questions over RDF data

ABSTRACT

Natural language query systems over RDF data need to rely on the semantic relations in query. First, we propose the new crowdsourcing model that used to produce semantic relations dataset. The model not only inherits completeness of the iterative model and accuracy of the parallel model, but also saves human resources. Second, we mine the rules of semantic relation recognition from the correlations between dependency structures and semantic relations. Third, we propose an algorithm of semantic relation recognition for natural language query over RDF data, and experiments demonstrate that it can recognize more semantic relations than existing methods.     

基于粗糙集理论的决策树方法在贷款客户信用评估中的应用

决策树是数据挖掘中常用的分类技术，其生成的规则便于决策者理解和应用。然而面对较多的属性且含有冗余和噪声属性的记录集生成的决策树时，无法删除冗余属性，造成运算过程复杂。本文旨在通过应用粗糙集理论，将其与决策树方法进行结合，对属性进行约简，降低运算复杂度，并生成相对简化的规则形式，并将其应用到银行个人贷款客户信用评估之中。     

The economics of land transfer and title insurance

In the presence of uncertainty over the title of land (due to fraud or error), a legal system can protect either the current (innocent) owner, or a previous owner who claims title. The predominant system in the United States generally awards title to the latter in the event of legitimate claim. Thus, current owners frequently purchase title insurance to provide indemnification in the event of a loss. In contrast, the Torrens system awards title to the current owner, but provides for indemnification of any legitimate claimants. We evaluate these two systems and argue that if transaction costs are low, both promote efficient assignment of title (according to the Coase Theorem), but if transaction costs are high, the Torrens system is more likely to award title to the party that values it the most (namely, the current possessor).     

Web数据挖掘技术探索与应用

Web数据挖掘是当今世界上的热门研究领域,本文从Web数据挖掘的定义开始分析研究Web数据挖掘的难点、过程等相关技术,并计论其在电子商务领域中的相关应用。     

中小企业股票市场化发行定价的半参数自组织模型与实证

随着深圳交易所中小企业板块的正式启动，我国中小企业新股发行的市场化定价方式必将启用。本文采用基于自组织数据挖掘GMDH算法的半参数模型的定价方法，将GMDH算法与半参数模型方法结合起来。不仅利用计算机的自动拟合优选最优复杂度模型，从而大大简化了非参数部分估计。创造了半参数模型崭新的实现方式，并达到了理想的定价效果，而且可以发挥半参数模型的特有优势。进行模型结构分析。经过实证和检验证明了这种模型用于我国股票发行定价的有效性和合理性。为完全市场化股票发行方式下确定股票的发行价格提供了新的方法。     

数据挖掘在信用风险管理中的应用

本文详细分析了在现代信用风险管理中不同方法对数据质量的要求,并在此基础上提出采用数据挖掘方法改进数据质量、提高计算精度的建议,供我国金融机构信用风险管理之借鉴。     

数据挖掘在高校体育数据分析中的应用

此文通过对数据挖掘技术的基本原理研究，分析和探讨了它在高校体育数据分析中的应用。     

脱贫与经济增长:"贵州新现象"探析——贵州云南两省的比较研究

经济增长通常意味着贫困率的下降,然而,对于增长很慢的经济体或经济增长没有带动贫困率降低的案例,这种假设似乎并不成立.中国两个较为贫困省份的数据展示了这样一个谜题:贵州省的经济增长缓慢但农村贫困率的下降较为显著,对这一现象不妨称之为"贵州新现象";与此同时,云南省经济高速增长但贫困率下降较为迟缓.根据相关考察和统计数据的分析,交通、流动人口、旅游业和矿产业四个因素是解释这个谜题的关键.这四个因素在两个省的不同分布和构造解释了它们经济增长与贫困率下降关系中的不平衡性.