Dat Mining Annotated Bibliography
Annotated Bibliography
Data Mining
Ayanso, A., & Yoogalingam, R. (2010). Profiling Retail Web Site Functionalities and Conversion Rates: A Cluster Analysis. International Journal of Electronic Commerce, 14(1), 79-113. doi:10.2753/JEC1086-4415140103
This article introduces the utilization of cluster analysis as a data mining tool. E-commerce has forced traditional businesses to reform their decision making processes and conduct its affairs based on activities occurring online. Monitoring web traffic is not a sufficient metric tool to measure success and therefore a system of conversion rates is utilized to determine profitability. Not everyone who visits a website purchases a product and the author describes several factors that lead to an unsuccessful visit to sales ratio. Retailers use websites to garner insight into customer activity and base decisions, but lack of sales conversions has prompted the author to conduct a cluster analysis between retailers that are solely web based and those that conduct business both from a storefront and online. Cluster analysis is a data mining technique that divides information into specific groups that provide insight and information for customer relationship management systems.
The authors of the article are Assistant Professors at Brock University with Doctorates in Information Systems and they demonstrate a mastery of how to properly perform a cluster analysis. The article was fairly comprehensive and easy to read and the intended audience of the article are business owners that wish to gain insight as to why their e-commerce activities are underperforming. Cluster analysis is a technique of data mining and I believe the step by step analysis conducted by the authors will serve as a practical guide to assist me in gaining fuller understanding of the process.
Baicoianu, A., & Dumitrescu, S. (2010). Data mining meets economic analysis: opportunities and challenges. Bulletin of the Transilvania University of
Data Mining
Ayanso, A., & Yoogalingam, R. (2010). Profiling Retail Web Site Functionalities and Conversion Rates: A Cluster Analysis. International Journal of Electronic Commerce, 14(1), 79-113. doi:10.2753/JEC1086-4415140103
This article introduces the utilization of cluster analysis as a data mining tool. E-commerce has forced traditional businesses to reform their decision making processes and conduct its affairs based on activities occurring online. Monitoring web traffic is not a sufficient metric tool to measure success and therefore a system of conversion rates is utilized to determine profitability. Not everyone who visits a website purchases a product and the author describes several factors that lead to an unsuccessful visit to sales ratio. Retailers use websites to garner insight into customer activity and base decisions, but lack of sales conversions has prompted the author to conduct a cluster analysis between retailers that are solely web based and those that conduct business both from a storefront and online. Cluster analysis is a data mining technique that divides information into specific groups that provide insight and information for customer relationship management systems.
The authors of the article are Assistant Professors at Brock University with Doctorates in Information Systems and they demonstrate a mastery of how to properly perform a cluster analysis. The article was fairly comprehensive and easy to read and the intended audience of the article are business owners that wish to gain insight as to why their e-commerce activities are underperforming. Cluster analysis is a technique of data mining and I believe the step by step analysis conducted by the authors will serve as a practical guide to assist me in gaining fuller understanding of the process.
Baicoianu, A., & Dumitrescu, S. (2010). Data mining meets economic analysis: opportunities and challenges. Bulletin of the Transilvania University of