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Customer Behavior Analysis on a Tmall E-commerce Shop
Renhao Jin,
Song Han,
Tao Liu,
Songnan Xi
Issue:
Volume 2, Issue 6, November 2016
Pages:
57-61
Received:
5 October 2016
Accepted:
11 November 2016
Published:
25 November 2016
DOI:
10.11648/j.ijdst.20160206.11
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Abstract: In recent years, China online marketing is very hot, and a lot of online shops run in the Tmall.com. This paper does an analysis of customer shopping behaviors in a certain e-commerce shop in Tmall. The shop is named X in this paper for privacy. Based on the descriptive analysis, it finds the profit customers and profit products in the shop. Then K-mean segmentation method is used to class the customers into 4 groups and the profiles of the customers in each group are described. The results on this paper can help the X shop to offer good services for the profit customers and do the precision marketing for all customers.
Abstract: In recent years, China online marketing is very hot, and a lot of online shops run in the Tmall.com. This paper does an analysis of customer shopping behaviors in a certain e-commerce shop in Tmall. The shop is named X in this paper for privacy. Based on the descriptive analysis, it finds the profit customers and profit products in the shop. Then K...
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A Hybrid Generic Algorithm for Dynamic Data Mining in Investment Decision Making
Kangzhi Yu,
Yufang Li,
Zhengying Cai
Issue:
Volume 2, Issue 6, November 2016
Pages:
62-71
Received:
16 October 2016
Accepted:
8 November 2016
Published:
9 December 2016
DOI:
10.11648/j.ijdst.20160206.12
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Abstract: To solve the risks and uncertainty problem in investment decision-making, a dynamic data mining architecture is introduced here. First, the investment decision-making process is examined and the involved risks are analyzed. Accordingly, dynamic data mining architecture is proposed here with the dynamic search ability of the generic algorithm. Second, a hybrid algorithm with dynamic learning ability is submitted to overcome the local minima problem prevalent in dynamic data mining. Whenever new data are generated, the data mining algorithm can dynamically collect the original input data without any reconstruction, to realize the dynamic update for investment decision-making. Last, an example is illustrated to verify the proposed model, and the solution provides us an effective model to improve the robustness of investment decision-making under risk environment.
Abstract: To solve the risks and uncertainty problem in investment decision-making, a dynamic data mining architecture is introduced here. First, the investment decision-making process is examined and the involved risks are analyzed. Accordingly, dynamic data mining architecture is proposed here with the dynamic search ability of the generic algorithm. Secon...
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On the Normalization of Data in Chinese Meanings
Luo Yan,
Wang Youqun,
Wu Yong
Issue:
Volume 2, Issue 6, November 2016
Pages:
72-75
Received:
24 October 2016
Accepted:
18 November 2016
Published:
20 December 2016
DOI:
10.11648/j.ijdst.20160206.13
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Abstract: Data normalization is important for data analysis so there are many methods of data normalization. Also because of many differences in various disciplines and fields, the concepts and methods of data normalization in literature appear vague and confusing in Chinese translations. In this paper, data normalization and data standardization are two main terms used on the basis of a detailed analysis of the situation. We recommend the "data normalization" corresponding to Chinese translations. Then theoretically, the differences between the two terms are elaborated and the intrinsic meanings and features of data normalization are studied.
Abstract: Data normalization is important for data analysis so there are many methods of data normalization. Also because of many differences in various disciplines and fields, the concepts and methods of data normalization in literature appear vague and confusing in Chinese translations. In this paper, data normalization and data standardization are two mai...
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A Framework for Evaluating Data Quality on Military Enterprise Networks
Lee P. Battle,
Edward F. Harrington
Issue:
Volume 2, Issue 6, November 2016
Pages:
76-83
Received:
15 August 2016
Accepted:
19 November 2016
Published:
21 December 2016
DOI:
10.11648/j.ijdst.20160206.14
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Abstract: This paper introduces a framework to determine data quality on enterprise networks for net-centric and net-ready initiatives as introduced by the US Department of Defense (DoD). Traditionally quality of data delivered to an enterprise user focuses on network performance, i.e. quality of service (QoS). It is proposed to add two new attributes pertaining to data sharing performance to QoS: data relevance (DR) and quality of data at source (QDS); and further a method to evaluate these new attributes. The QDS attribute brings distinction to the resultant data quality of the network's quality of service. This distinction is necessary to reflect the separation in procurement and management for sensor systems and network systems for the DoD. The DR attribute is introduced; it is important in enabling enterprise data consumers to sort, filter and prioritize data. There is also a need to assess the quality of data sharing across the enterprise network. One recent method subjectively assess the quality of data is to measure the user satisfaction referred to as quality of experience (QoE). The QoE is assessed for each of the framework’s attributes using the best practices from survey statistics in sampling and estimation. The overall value of data quality on enterprise networks is decided using a minimax decision model consisting of the three attributes. The resultant minimax value correlates to the lowest performing attributes of the framework. The minimax decision model is chosen to meet the design philosophy that little advantage to the overall enterprise network performance will result from further investment in high performing attributes prior to balancing performance across all three model attributes. The presented framework offers decision support tools to enable agencies to allocate limited resources towards improving the performance of their net-centric service offerings to the enterprise network.
Abstract: This paper introduces a framework to determine data quality on enterprise networks for net-centric and net-ready initiatives as introduced by the US Department of Defense (DoD). Traditionally quality of data delivered to an enterprise user focuses on network performance, i.e. quality of service (QoS). It is proposed to add two new attributes pertai...
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