عنوان مقاله
استفاده از داده کاوی وب برای بهبود طراحی وبسایت های تجارت الکترونیکی
فهرست مطالب
مقدمه
کمبود آب و مزایای نسبی
مدل جدید GTAP-W
طراحی سناریوهای شبیه سازی
بحث و نتایج
نتیجه گیری
بخشی از مقاله
کشف زیرگروه از طریق الگوریتم : NMEEF-SDهدف اصلی کشف زیرگروه اینست که روابط را داده ها با توجه به یک خصوصیت پیدا کند. ما خصوصا برای این مسئله، خصوصیاتی را مانند کلمات کلیدی و منبع نوع بازدید کننده را تحلیل میکنیم.
در زیر، وابسته ترین زیرگروه های بدست آمده برای الگوریتم NMEEF-SD با توجه به مقادیر خصوصیتی مختلف با مقادیر سنجش های کیفیت در جدول 1نشان داده شده است. و این قوانین بدست آمده و سنجش های اهمیت کیفیت، غیرعادی بودن، حساسیت، و اطمینان نامعلوم را توصیف میکند.
کلمات کلیدی:
Web usage mining to improve the design of an e-commerce website: OrOliveSur.com C.J. Carmona a,⇑ , S. Ramírez-Gallego a , F. Torres b , E. Bernal c , M.J. del Jesus a , S. García a aDepartment of Computer Science, University of Jaén, 23071 Jaén, Spain bDepartment of Marketing, University of Jaén, 23071 Jaén, Spain cDepartment of Economics, University of Jaén, 23071 Jaén, Spain article info Keywords: Web usage mining OrOliveSur.com Subgroup discovery Association rules Clustering abstract Web usage mining is the process of extracting useful information from users history databases associated to an e-commerce website. The extraction is usually performed by data mining techniques applied on server log data or data obtained from specific tools such as Google Analytics. This paper presents the methodology used in an e-commerce website of extra virgin olive oil sale called www.OrOliveSur.com. We will describe the set of phases carried out including data collection, data preprocessing, extraction and analysis of knowledge. The knowledge is extracted using unsupervised and supervised data mining algorithms through descriptive tasks such as clustering, association and subgroup discovery; applying classical and recent approaches. The results obtained will be discussed especially for the interests of the designer team of the website, providing some guidelines for improving its usability and user satisfaction. 2012 Elsevier Ltd. All rights reserved. 1. Introduction Electronic commerce is the buying and selling of products or services through electronic media, such as Internet and other computer networks. Originally, the term was applied to the execution of transactions through electronic transactions such as electronic data interchange. However, with the advent of Internet in the mid 90’s, it began mainly referring to the sale of goods and services on Internet, primarily using electronic payment. The amount of trade conducted electronically has grown extraordinarily since the spread of Internet. A high variety of commerce is made in this way (Soares, Peng, Meng, Washio, & Zhou, 2008), stimulating the creation and use of innovations such as electronic funds transfer, the supply chain management, marketing on Internet, online transaction processing, electronic exchange data, systems, inventory management and automated data collection. After the concentration of olive oil cooperatives in Andalusia (Spain), in the last years, the literature proliferates on the export of olive products (Moral-Pajares & Lanzas-Molina, 2009), the use of e-commerce in the agricultural cooperatives and the adoption of Information and Communication Technologies as an essential tool in such export. Interesting studies on the international market and demand for olive oil were conducted by Mili and Zuniga (2003). Also, it is worth mentioning the discussion of factors affecting consumer demand for olive oil by selection bias models based on the Heckman correction (Tsakiridou, Mattas, & Tzimitra-Kalogianni, 2006). In addition, other contributions concerning studies located outside Spain, advocates of consumer satisfaction and costs of olive oil (Krystallis, Fotopoulos, & Zotos, 2006). Blery and Kapsopoulou (2007) conducted a study related to the promotion and marketing of a Greek company, olive oil and predict that exports are needed to increase sales. The need arises to propose methodologies for intelligent data analysis, to enable the extraction of useful knowledge from the data (Fayyad, Piatetsky-Shapiro, & Smyth, 1996). This is the concept of the Knowledge Discovery in Databases (KDD), which can be defined as the nontrivial process of identifying patterns in data with the following characteristics: valid, novel, useful and understandable (Han, 2005). The KDD process is a set of interactive and iterative steps, including among them the pre-processing of the data to correct inaccuracies, incompleteness or inconsistency present, reducing the number of records or finding the most representative features. KDD combines the traditional techniques of knowledge extraction with numerous resources developed in the area of artificial intelligence. In the specialized literature, we found recent applications and consolidated reviews on the use of data mining in e-commerce. In Schafer, Konstan, and Riedl (2001), the authors discussed different models of e-commerce recommendation and in Hu and Liu (2004) amethodology to extract information from customer questionnaires was provided. The extraction of predictive knowledge is used to set personalized recommendations in web use (Zhang & Jiao, 2007) and association rules are used for descriptive same task (Lazcorreta, Botella, & Fernandez-Caballero, 2008). Predictive and descriptive tasks can hybridize to achieve the same purpose (Kim, Cho, Kim, 0957-4174/$ - see front matter 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.eswa.2012.03.046 ⇑ Corresponding author. Tel.: +34 953 211956; fax: +34 953 212472. E-mail address: ccarmona@ujaen.es (C.J. Carmona). Expert Systems with Applications 39 (2012) 11243–11249
