<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/11782" mdate="2009-02-23 00:00:00"> <author>Hamidah Jantan and Abdul Razak Hamdan and Zulaiha Ali Othman</author> <title>Knowledge Discovery Techniques for Talent Forecasting in Human Resource Application</title> <pages>178 - 186</pages> <year>2009</year> <volume>3</volume> <number>2</number> <journal>International Journal of Industrial and Manufacturing Engineering</journal> <ee>https://publications.waset.org/pdf/11782</ee> <url>https://publications.waset.org/vol/26</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>Human Resource (HR) applications can be used to provide fair and consistent decisions, and to improve the effectiveness of decision making processes. Besides that, among the challenge for HR professionals is to manage organization talents, especially to ensure the right person for the right job at the right time. For that reason, in this article, we attempt to describe the potential to implement one of the talent management tasks i.e. identifying existing talent by predicting their performance as one of HR application for talent management. This study suggests the potential HR system architecture for talent forecasting by using past experience knowledge known as Knowledge Discovery in Database (KDD) or Data Mining. This article consists of three main parts; the first part deals with the overview of HR applications, the prediction techniques and application, the general view of Data mining and the basic concept of talent management in HRM. The second part is to understand the use of Data Mining technique in order to solve one of the talent management tasks, and the third part is to propose the potential HR system architecture for talent forecasting.</abstract> <index>Open Science Index 26, 2009</index> </article>