scholarly journals Combination and Application of Data Mining in Knowledge Management of Colleges and Universities

2015 ◽  
Author(s):  
Xueping Liu
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Colleges And Universities

scholarly journals Using Data Mining Techniques to Develop Knowledge Management in Organizations: A Review.

2017 ◽  
Vol 7 (2) ◽  
pp. 80-89
Author(s):  
Bagher Dastyar ◽  
◽  
Hanieh Kazemnejad ◽  
Alireza Asgari Sereshgi ◽  
Mohammad Amin Jabalameli ◽  
...  
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Data Mining Techniques ◽  
Using Data

Data mining in anti-money laundering field

2017 ◽  
Vol 20 (3) ◽  
pp. 301-310 ◽  
Author(s):  
Noriaki Yasaka
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Money Laundering ◽  
Design Methodology ◽  
Point Of View ◽  
Important Task ◽  
Content Type ◽  
Suspicious Transaction ◽  
Mining Methods ◽  
The Creation

Purpose This report aims to focus on how suspicious transaction report is created with data mining methods and used from the point of view of knowledge management. Design/methodology/approach This paper considers data mining versus knowledge management in the anti-money laundering (AML) field. Findings In the AML field, the information and knowledge gained are not necessarily used for or shared with the related shareholders. Creating and co-evolving the network of “knowledge professionals” is the impending assignment in this industry. The first and most important task is knowledge management in the global AML field. Originality/value The report considers the creation with data mining methods and utilization from the point of view of knowledge management.

The Role of Data Mining for Business Intelligence in Knowledge Management

2015 ◽  
pp. 12-33 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Human Resources ◽  
Organizational Performance ◽  
Business Intelligence ◽  
Statistical Significance ◽  
Practical Applications ◽  
Business Goals ◽  
Emerging Trends

This chapter introduces the role of Data Mining (DM) for Business Intelligence (BI) in Knowledge Management (KM), thus explaining the concept of KM, BI, and DM; the relationships among KM, BI, and DM; the practical applications of KM, BI, and DM; and the emerging trends toward practical results in KM, BI, and DM. In order to solve existing BI problems, this chapter also describes practical applications of KM, BI, and DM (in the fields of marketing, business, manufacturing, and human resources) and the emerging trends in KM, BI, and DM (in terms of larger databases, high dimensionality, over-fitting, evaluation of statistical significance, change of data and knowledge, missing data, relationships among DM fields, understandability of patterns, integration of other DM systems, and users' knowledge and interaction). Applying DM for BI in the KM environments will enhance organizational performance and achieve business goals in the digital age.

Opportunities for Data Mining and Customer Knowledge Management for Shopping Centres

2003 ◽  
pp. 154-172
Author(s):  
Charles Dennis ◽  
David Marsland ◽  
Tony Cockett
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Management System ◽  
Knowledge Management System ◽  
Shopping Centers ◽  
Customer Knowledge ◽  
Management Study ◽  
Customer Knowledge Management ◽  
The Subject ◽  
The Uk

Shopping centers are an important part of the UK economy and have been the subject of considerable research. Relying on complex interdependencies between shoppers, retailers and owners, shopping centers are ideal for knowledge management study. Nevertheless, although retailers have been in the forefront of data mining, little has been written on customer knowledge management for shopping centers. In this chapter, the authors aim to demonstrate the possibilities and draw attention to the possible implications of improving customer satisfaction. Aspects of customer knowledge management for shopping centers are considered using analogies drawn from an exploratory questionnaire survey. The objectives of a customer knowledge management system could include increasing rental incomes and bringing new life back into shopping centers and towns.

Knowledge Management in E-Government

2011 ◽  
pp. 2361-2367 ◽  
Author(s):  
Deborah S. Carstens ◽  
LuAnn Bean ◽  
Judith Barlow
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Balanced Scorecard ◽  
Policy Development ◽  
Public Services ◽  
Codes Of Conduct ◽  
Sensitive Information ◽  
Organizational Challenges ◽  
Comprehensive Management ◽  
Increasing Demand

Over the past decade, government has created innovative and complex systems connecting people to information by focusing on Knowledge Management (KM) practices. KM, described as the comprehensive management of an organization’s expertise through collecting, categorizing and disseminating knowledge, leads to knowledge discovery through techniques such as data mining. These developments have transformed traditional access to public services into e-government. Ever increasing demand to access and information has also brought about e-government policy development challenges for integrative KM practices in public services (Riege & Lindsay, 2006). In particular, the size and complexity of governmental structures and the vast data stores have become problematic (Koh, Ryan, & Prybutok, 2005). Because government uses, collects, processes, and disseminates sensitive information containing personal, financial and medical data, it is very easy for organizations to reprocess the information and disseminate it (Hewett & Whitaker, 2002). Ebrahim and Irani (2005) state that the benefits gained by data mining and KM practices are erased when information is not viewed as confidential but instead as a commodity to be bought and sold. Therefore, e-government must uphold a higher standard of ethics in KM practices through continued development of codes of conduct and governance policies for data that build citizen trust and ensure success of e-government services and transactions (Verschoor, 2000). An excellent framework to effectively preserve this trust is a balanced scorecard (BSC), which was first introduced by Kaplan and Norton (1992, 1996a, 1996b). The framework serves to continuously improve the KM process when modified for e-government. Therefore, this chapter describes technological and organizational challenges faced by e-government in KM and retrieval and presents the BSC framework to overcome these challenges.

Sign in / Sign up

Export Citation Format

Share Document