The contribution of data mining to information science

The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research

Application of artificial neural network in market segmentation: A review on recent trends

Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table

A Personalized Commodities Recommendation Procedure and Algorithm Based on Association Rule Mining

The double-quick growth of EB has caused commodities overload, where our customers are not longer able to efficiently choose the products adapt to them. In order to overcome the situation that both companies and customers are facing, we present a personalized recommendation, although several recommendation systems which may have some disadvantages have been developed. In this paper, we focus on the association rule mining by EFFICIENT algorithm which can simple discovery rapidly the all association rules without any information loss. The EFFICIENT algorithm which comes of the conventional Aprior algorithm integrates the notions of fast algorithm and predigested algorithm to find the interesting association rules in a given transaction data sets. We believe that the procedure should be accepted, and experiment with real-life databases show that the proposed algorithm is efficient one

Investigation of the use of navigation tools in web-based learning: A data mining approach

Web-based learning is widespread in educational settings. The popularity of Web-based learning is in great measure because of its flexibility. Multiple navigation tools provided some of this flexibility. Different navigation tools offer different functions. Therefore, it is important to understand how the navigation tools are used by learners with different backgrounds, knowledge, and skills. This article presents two empirical studies in which data-mining approaches were used to analyze learners' navigation behavior. The results indicate that prior knowledge and subject content are two potential factors influencing the use of navigation tools. In addition, the lack of appropriate use of navigation tools may adversely influence learning performance. The results have been integrated into a model that can help designers develop Web-based learning programs and other Web-based applications that can be tailored to learners' needs

Association Rules in Data Mining: An Application on a Clothing and Accessory Specialty Store

Retailers provide important functions that increase the value of the products and services they sell to consumers. Retailers value creating functions are providing assortment of products and services: breaking bulk, holding inventory, and providing services. For a long time, retail store managers have been interested in learning about within and cross-category purchase behavior of their customers, since valuable insights for designing marketing and/or targeted cross-selling programs can be derived. Especially, parallel to the development of information processing and communication technologies, it has become possible to transfer customers shopping information into databases with the help of barcode technology. Data mining is the technique presenting significant and useful information using of lots of data. Association rule mining is realized by using market basket analysis to discover relationships among items purchased by customers in transaction databases. In this study, association rules were estimated by using market basket analysis and taking support, confidence and lift measures into consideration. In the process of analysis, by using of data belonging to the year of 2012 from a clothing and accessory specialty store operating in the province of Osmaniye, a set of data related to 42.390 sales transactions including 9.000 different product kinds in 35 different product categories (SKU) were used. Analyses were carried out with the help of SPSS Clementine packet program and hence 25.470 rules were determined