https://minerva-access.unimelb.edu.au/bitstream/handle/11343/118668/Multi-critea decision_24.pdf

Multi-criteria decision making approaches for supplier evaluation and selection: A literature review

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

/pdf/educational-data-mining-a-review-of-the-state-of-the-art-51aglgywl8.pdf

Educational Data Mining: A Review of the State of the Art

Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.

https://isiarticles.com/bundles/Article/pre/pdf/22098.pdf

Educational data mining: A survey from 1995 to 2005

Supplier selection, the process of finding the right suppliers who are able to provide the buyer with the right quality products and/or services at the right price, at the right time and in the right quantities, is one of the most critical activities for establishing an effective supply chain. On the other hand, it is a hard problem since supplier selection is typically a multi criteria group decision-making problem involving several conflicting criteria on which decision maker's knowledge is usually vague and imprecise. In this study, TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate supplier in group decision making environment. Intuitionistic fuzzy weighted averaging (IFWA) operator is utilized to aggregate individual opinions of decision makers for rating the importance of criteria and alternatives. Finally, a numerical example for supplier selection is given to illustrate application of intuitionistic fuzzy TOPSIS method.

A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method

Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the period of 2000-2006 covering 24 journals and proposes a classification scheme to classify the articles. Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM. Eighty-seven articles were subsequently selected, reviewed and classified. Each of the 87 selected papers was categorized on four CRM dimensions (Customer Identification, Customer Attraction, Customer Retention and Customer Development) and seven data mining functions (Association, Classification, Clustering, Forecasting, Regression, Sequence Discovery and Visualization). Papers were further classified into nine sub-categories of CRM elements under different data mining techniques based on the major focus of each paper. The review and classification process was independently verified. Findings of this paper indicate that the research area of customer retention received most research attention. Of these, most are related to one-to-one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM. Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.

http://cjou.im.tku.edu.tw/bi2009/DM-usage.pdf

Review: Application of data mining techniques in customer relationship management: A literature review and classification

This article outlines a hybrid method, incorporating multiple techniques into an evaluation process, in order to select competitive suppliers in a supply chain. It enables a purchaser to do single sourcing and multiple sourcing by calculating a combined supplier score (CSS), which accounts for both qualitative and quantitative factors that impact on supply chain performance. By performing a cluster analysis, it draws a supplier map (SM) so as to position suppliers within the qualitative and quantitative dimensions of performance efficiency, and to select a portfolio of suppliers from supplier segments, which are different in performance with regard to key factors.

https://isiarticles.com/bundles/Article/pre/pdf/19147.pdf

A hybrid approach to supplier selection for the maintenance of a competitive supply chain

Data mining, which is also referred to as knowledge discovery in databases, is the process of extracting valid, previously unknown, comprehensible and actionable information from large databases and using it to make crucial business decisions. In this paper, we present the data mining process from data extraction to knowledge interpretation and data mining tasks, and corresponding algorithms. Before applying data mining techniques to a real-world application, we build a data mart on the enterprise Intranet. RFM (recency, frequency, and monetary) data extracted from the data mart are used extensively for our analysis. We then propose a new marketing strategy that fully utilizes the knowledge resulting from data mining.

Application of data mining tools to hotel data mart on the Intranet for database marketing

The Web-based personalization system proposed here uses both collaborative filtering and Web usage mining to give online shoppers the personalized recommendations they need to purchase products more intelligently.

Helping online customers decide through Web personalization

Application of data mining techniques to the WWW (World Wide Web), referred to as Web mining, has been the focus of several research projects and papers. One of several possibilities can be its application to distance education. Taken as a whole, the emerging trends in distance education are facilitating its usability on the Internet. With the explosive growth of information sources available on the WWW, Web mining has become suitable for keeping pace with the trends in education, such as mass customization. In this paper, we define Web mining and present an overview of distance education. We describe the possibilities of application of Web mining to distance education, and, consequently, show that the use of Web mining for educational purposes is of great interest.

Sung Ho Ha

Papers

A hybrid approach to supplier selection for the maintenance of a competitive supply chain

Application of data mining tools to hotel data mart on the Intranet for database marketing

Helping online customers decide through Web personalization

Web mining for distance education

Customer's time-variant purchase behavior and corresponding marketing strategies: an online retailer's case