Knowledge management vs. data mining: Research trend, forecast and citation approach

Knowledge management (KM) and data mining (DM) have become more important today, however, there are few comprehensive researches and categorization schemes to discuss the characteristics for both of them. Using a bibliometric approach, this paper analyzes KM and DM research trends, forecasts and citations from 1989 to 2009 by locating headings ''knowledge management'' and ''data mining'' in topics in the SSCI database. The bibliometric analytical technique was used to examine these two topics in SSCI journals from 1989 to 2009, we found 1393 articles with KM and 1181 articles with DM. This paper implemented and classified KM and DM 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 KM and DM technologies have developed in this period and to analyze KM and DM technology tendencies under the above result. Also, the paper performs the K-S test to check whether the distribution of author article production follows Lotka's law. The research findings can be extended to investigate author productivity by analyzing variables such as chronological and academic age, number and frequency of previous publications, access to research grants, job status, etc. In such a way characteristics of high, medium and low publishing activity of authors can be identified. Besides, these findings will also help to judge scientific research trends and understand the scale of development of research in KM and DM through comparing the increases of the article author. Based on the above information, governments and enterprises may infer collective tendencies and demands for scientific researcher in KM and DM to formulate appropriate training strategies and policies in the future. This analysis provides a roadmap for future research, abstracts technology trend information and facilitates knowledge accumulations, therefore the future research can concentrated in core categories. This implies that the phenomenon ''success breeds success'' is more common in higher quality publications.

[1]  William Gray Potter,et al.  Lotka's Law Revisited , 1981 .

[2]  Michael Y. Hu,et al.  Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis , 1999, Eur. J. Oper. Res..

[3]  Ikujiro Nonaka,et al.  From information processing to knowledge creation: A Paradigm shift in business management , 1996 .

[4]  Hsu-Hao Tsai,et al.  Research trends analysis by comparing data mining and customer relationship management through bibliometric methodology , 2011, Scientometrics.

[5]  Paul H. J. Hendriks,et al.  Knowledge-based systems and knowledge management: Friends or foes? , 1999, Inf. Manag..

[6]  Henry C. W. Lau,et al.  Design and implementation of an integrated knowledge system , 2003, Knowl. Based Syst..

[7]  D. K. Gupta,et al.  Lotka's law and productivity patterns of entomological research in Nigeria for the period, 1900–1973 , 1987, Scientometrics.

[8]  William G. Potter,et al.  "Of Making Many Books There Is No End": Bibliometrics and Libraries. , 1988 .

[9]  Efraim Turban,et al.  Decision Support and Business Intelligence Systems (8th Edition) , 2006 .

[10]  姜國輝,et al.  E-Commerce Research Trend Forecasting: A Study of Bibliometric Methodology , 2011 .

[11]  Jay Liebowitz,et al.  Knowledge management and its link to artificial intelligence , 2001, Expert Syst. Appl..

[12]  Robert de Hoog,et al.  Supporting knowledge management: A selection of methods and techniques , 1997 .

[13]  Jon-Arild Johannessen,et al.  Aspects of innovation theory based on knowledge-management , 1999 .

[14]  Chao-Ton Su,et al.  Knowledge Mining from Trained Neural Networks , 2002, J. Comput. Inf. Syst..

[15]  Peter Weingart,et al.  Evaluation of Research Performance. The Danger of Numbers , 2003 .

[16]  Gertjan van Heijst,et al.  Corporate memories as a tool for knowledge management , 1997 .

[17]  Gregory Piatetsky-Shapiro,et al.  The KDD process for extracting useful knowledge from volumes of data , 1996, CACM.

[18]  Paul Travis Nicholls.  Bibliometric modeling processes and the empirical validity of Lotka's Law , 1989, JASIS.

[19]  Yen-Ping Chi,et al.  An empirical study of research trends and forecasts: Customer relationship management , 2012 .

[20]  M. Polanyi.  Chapter 7 – The Tacit Dimension , 1997 .

[21]  T. V. Leeuwen,et al.  Assessment of the scientific basis of interdisciplinary, applied research: Application of bibliometric methods in Nutrition and Food Research , 2002 .

[22]  Evangelos Simoudis,et al.  Mining business databases , 1996, CACM.

[23]  Alex Berson,et al.  Building Data Mining Applications for CRM , 1999 .

[24]  Russell C. Coile,et al.  Lotka's frequency distribution of scientific productivity , 1977, J. Am. Soc. Inf. Sci..

[25]  季延平,et al.  Trend analysis of supply chain management by bibliometric methodology , 2011 .

[26]  Anthony F. J. van Raan,et al.  Advanced bibliometric methods as quantitative core of peer review based evaluation and foresight exercises , 1996, Scientometrics.

[27]  Miranda Lee Pao,et al.  Lotka's law: A testing procedure , 1985, Inf. Process. Manag..

[28]  J. Vlachý,et al.  Frequency distributions of scientific performance a bibliography of Lotka's law and related phenomena , 1978, Scientometrics.

[29]  Desmond Fletcher,et al.  Forecasting with neural networks: An application using bankruptcy data , 1993, Inf. Manag..

[30]  David Adam,et al.  Citation analysis: The counting house , 2002, Nature.

[31]  Michael J. A. Berry,et al.  Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management , 2004 .

[32]  Stephen A.W. Drew,et al.  Building Knowledge Management into Strategy: Making Sense of a New Perspective , 1999 .

[33]  Herbert A. Simon,et al.  Applications of machine learning and rule induction , 1995, CACM.

[34]  K. Wiig.  Knowledge management: Where did it come from and where will it go? , 1997 .

[35]  Hsu-Hao Tsai,et al.  Global data mining: An empirical study of current trends, future forecasts and technology diffusions , 2012, Expert Syst. Appl..

[36]  Bert van Wegen,et al.  Understanding and valuing knowledge assets: Overview and method , 1997 .

[37]  Henk F. Moed,et al.  Improving the accuracy of Institute for Scientific Information's journal impact factors , 1995 .

[38]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[39]  Alfred J. Lotka,et al.  The frequency distribution of scientific productivity , 1926 .

[40]  Melody Y. Kiang,et al.  Managerial Applications of Neural Networks: The Case of Bank Failure Predictions , 1992 .

[41]  Peter Weingart,et al.  Impact of bibliometrics upon the science system: Inadvertent consequences? , 2005, Scientometrics.

[42]  Behrooz Karimi,et al.  Modeling and evaluating the strategic effects of improvement programs on the manufacturing performance using neural networks , 2010 .

[43]  Shu-Hsien Liao,et al.  Problem solving and knowledge inertia , 2002, Expert Syst. Appl..

[44]  I. K. Ravichandra Rao,et al.  The distribution of scientific productivity and social change , 1980, J. Am. Soc. Inf. Sci..

[45]  A. Pritchard,et al.  Statistical bibliography or bibliometrics , 1969 .

[46]  Jay Liebowitz,et al.  Knowledge-based systems and knowledge management , 2001 .

[47]  J. Liebowitz,et al.  Does measuring knowledge make “cents”? , 1999 .

[48]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

[49]  B. Rubenstein-Montano,et al.  A systems thinking framework for knowledge management , 2001, Decis. Support Syst..

[50]  Robert N. Broadus.  Toward a definition of “bibliometrics” , 1987, Scientometrics.

[51]  J. Efrim Boritz,et al.  Effectiveness of neural network types for prediction of business failure , 1995 .

[52]  E. Mine Cinar,et al.  Neural Networks: A New Tool for Predicting Thrift Failures , 1992 .

[53]  Miguel A. P. M. Lejeune,et al.  Measuring the impact of data mining on churn management , 2001, Internet Res..