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.     

R&D strategy and Australian manufacturing industry: an empirical investigation of emphasis and effectivness

This research investigates the degree of emphasis placed by the Australian manufacturing industry on Research and Development (R&D) strategy as well as on other organisational strategies such as marketing, HRs (HR), technology, and operations at the functional level. The research extends further to investigate whether the emphasis on R&D strategy differs with contextual factors such as firm size, firm’s generic strategy, type of market, firm’s life cycle stage, etc. A mail survey was carried out to collect information from manufacturing firms across Australia. This was followed by an on-site interview of some of the senior managers of manufacturing firms from three Australian states: South Australia, Victoria and New South Wales.The study found that although the emphasis on R&D strategy has received a high priority in the past few years, it has not been as effective as the operations strategy and the technology strategy. The operations strategy has been accorded the highest importance in the past few years and this will continue in the future. The study also indicated that firms, which adopt a differentiation generic strategy, put more emphasis on R&D than the firms, which adopt cost leadership and focus generic strategies. The results did not indicate any evidence of a relationship between firm size and the R&D strategy although large firms are found to put more emphasis on operations and marketing strategies. Relatively higher emphasis was placed on the R&D strategy by businesses: at the growth stage; involved in the manufacture of consumer goods as opposed to industrial goods; involved in exports with a higher sales growth in export markets; and having a higher growth rate relative to their industry.     

Internal labour markets in the Australian banking industry: their nature prior to the Second World War and their recent decline

This paper uses evidence from late nineteenth-and early twentieth-century personnel records of two Australian banks to examine the nature of internal labour markets prior to the Second World War. It is argued that the industry possessed all the classic features of internal labour markets: limited ports of entry, internal promotion, long careers, and assignment of wages by well-defined rules. The paper then examines the reasons why banks adopted internal labour markets. Finally, the paper examines the recent decline of internal labour markets and examines the role of technological and social changes in this decline.     

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。     

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。