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Analysing the Density of Subgroups in Valued Relationships Based on DNA Computing

  • Ikno Kim
  • Don Jyh-Fu Jeng
  • Junzo Watada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4253)

Abstract

One method for enhancing the quality of work life for companies or other organisations is to rearrange employees by detecting and analysing employees’ close interpersonal relationships based on business implications. Although human resource managers can use various methods to enhance the quality of work life, one of the most widely used and effective methods is job rotation. In this paper, we select a model of a workplace where employees in a variety of job functions are sharing tasks, information, etc. through close interpersonal relationships, and we suppose a personnel network which contains strong terms of mutual understanding. However, with a huge number of employees it becomes extremely difficult to find the maximum clique of employees for rearrangement, meaning this is NP-hard. Therefore, we employ DNA computing, also known as molecular computation, to this rearranging problem. The goal of this paper is to propose a way to apply DNA computing to this human resource management problem, and to measure its effectiveness in rearranging employees to analyse the density of subgroups in a personnel network with valued relationships.

Keywords

Human Resource Manager Molecular Computation Maximal Clique Problem Cohesive Subgroup Close Interpersonal Relationship 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ikno Kim
    • 1
  • Don Jyh-Fu Jeng
    • 1
  • Junzo Watada
    • 1
  1. 1.Graduate School of Information, Production and SystemsWaseda UniversityFukuokaJapan

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