دسته : همه

عنوان انگلیسی مقاله:

Global data mining: An empirical study of current trends, future forecasts and technology diffusions

ترجمه عنوان مقاله: داده کاوی جهانی: مطالعه تجربی از روند فعلی پیش بینی آینده و انتشار فناوری

رشته: مدیریت دانش، فناوری اطلاعات

سال انتشار: 2012

تعداد صفحات مقاله انگلیسی: 10 صفحه

منبع: الزویر و ساینس دایرکت

نوع فایل: pdf

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چکیده انگلیسی مقاله

Using a bibliometric approach, this paper analyzes research trends and forecasts of data mining from 1989 to 2009 by locating heading ‘‘data mining’’ in topic in the SSCI database. The bibliometric analytical technique was used to examine the topic in SSCI journals from 1989 to 2009, we found 1181 articles with data mining. This paper implemented and classified data mining articles using the following eight categories publication year, citation, country/territory, document type, institute name, language, source title and subject area for different distribution status in order to explore the differences and how data mining technologies have developed in this period and to analyze technology tendencies and forecasts of data mining under the above results. Also, the paper performs the K-S test to check whether the analysis follows Lotka’s law. Besides, the analysis also reviews the historical literatures to come out technology diffusions of data mining. The paper provides a roadmap for future research, abstracts technology trends and forecasts, and facilitates knowledge accumulation so that data mining researchers can save some time since core knowledge will be concentrated in core categories. This implies that the phenomenon ‘‘success breeds success’’ is more common in higher quality publications.

Keywords: Data mining, Research trends and forecasts, Technology diffusions, Bibliometric methodology

مقدمه انگلیسی مقاله

Data mining is an interdisciplinary field that combines artificial intelligence, database management, data visualization, machine learning, mathematic algorithms, and statistics. Data mining, also known as knowledge discovery in databases (KDD) (Chen, Han, & Yu, 1996; Fayyad, Piatetsky-Shapiro, & Smyth, 1996a), is a rapidly emerging field. This technology provides different methodologies for decision-making, problem solving, analysis, planning, diagnosis, detection, integration, prevention, learning, and innovation This technology is motivated by the need of new techniques to help analyze, understand or even visualize the huge amounts of stored data gathered from business and scientific applications. It is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structures from large amounts of data stored in databases, data warehouses, or other information repositories. It can be used to help companies to make better decisions to stay competitive in the marketplace. The major data mining functions that are developed in commercial and research communities include summarization, association, classification, prediction and clustering. These functions can be implemented using a variety of technologies, such as database-oriented techniques, machine learning and statistical techniques (Fayyad, Piatetsky-Shapiro, & Smyth, 1996b).

Data mining was defined by Turban, Aronson, Liang, and Sharda (2007, p.305) as a process that uses statistical, mathematical, artificial intelligence and machine-learning techniques to extract and identify useful information and subsequently gain knowledge from large databases. In an effort to develop new insights into practiceperformance relationships, data mining was used to investigate improvement programs, strategic priorities, environmental factors, manufacturing performance dimensions and their interactions (Hajirezaie, Husseini, Barfourosh, et al., 2010). Berson, Smith, and Thearling (2000), Lejeune (2001), Ahmed (2004) and Berry and Linoff (2004) also defined data mining as the process of extracting or detecting hidden patterns or information from large databases. With an enormous amount of customer data, data mining technology can provide business intelligence to generate new opportunities (Bortiz & Kennedy, 1995; Fletcher & Goss, 1993; Langley & Simon, 1995; Lau, Wong, Hui, & Pun, 2003; Salchenberger, Cinar, & Lash, 1992; Su, Hsu, & Tsai, 2002; Tam & Kiang, 1992; Zhang, Hu, Patuwo, & Indro, 1999).

Recently, a number of data mining applications and prototypes have been developed for a variety of domains (Brachman, Khabaza, Kloesgen, Piatetsky-Shapiro, & Simoudis, 1996) including marketing, banking, finance, manufacturing and health care. In addition, data mining has also been applied to other types of data such as time-series, spatial, telecommunications, web, and multimedia data. In general, the data mining process, and the data mining technique and function to be applied depend very much on the application domain and the nature of the data available.

Using a bibliometric approach, the paper analyzes technology trends and forecasts of data mining from 1989 to 2009 by locating heading ‘‘data mining’’ in topic in the SSCI database. This paper surveys and classifies data mining articles using the following eight categories – publication year, citation, document type, country/territory, institute name, language, source title and subject area – for different distribution status in order to explore the difference and how technologies and applications of data mining have developed in this period and to analyze technology trends and forecasts of data mining under the above results. Besides, the analysis also reviews the historical literatures to come out technology diffusions of data mining.

The analysis provides a roadmap for future research, abstracts technology trends and forecasts, and facilitates knowledge accumulation so that data mining researchers can save some time since core knowledge will be concentrated in core categories. This implies that the phenomenon ‘‘success breeds success’’ is more common in higher quality publications.

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