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.     

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。     

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。     

传统跨国公司理论及其发展路径

二战后，特别是20世纪60年代以来，跨国公司进入了高速发展时期。不仅跨国公司的规模不断扩大，实力日益增强，而且其海外子公司数目迅速增加，海外生产经营比重也直线上升。跨国公司的发展引起了西方学者的极大关注，以此为研究对象发表了大量的论著，提出了一系列旨在阐明跨国公司对外扩张动因及其余件的理论和模型，已经形成了数十种跨国公司理论流派。本文对其中具有代表性的跨国公司理论作一个系统的回顾。     

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.     

基于云计算与物联网技术的数据挖掘

在科学技术快速发展的今天,信息通信行业已经与我们的日常生活完全融为一体,并且推动了整个社会的发展,所以在科学技术快速进步的过程中,信息通信相关技术也在不断发展,其中,基于云计算的物联网发展越来越受到人们的重视。论文对基于云计算的物联网数据挖掘模式进行分析及探讨,从而为互联网数据挖掘技术发展提供相应的参考。     

铜陵矿区生态经济建设

本文根据矿区生态经济系统协调发展和演化的原理,提出合理规划、建设铜陵生态市的建议,以实现铜陵经济、社会的可持续发展。