CHAPTER ONE
INTRODUCTION
1.0 Introduction
Data Mining (DM) really gained a lot of prominence in the society as it helped make prediction methodologies easier in various fields. Data mining may be viewed as the extraction of patterns and models from observed data. Data mining tools aid the discovery of patterns in data. Gartner, the global leader in technology research and IT services define mining as the process of discovering meaningful correlations, patterns and trends by sifting through large amount of data stored in depositories. Any data base or data ware house that is rich and colorful with information has to be mined for intelligent decision making. Over the years, various techniques have evolved in DM namely machine learning, statistics, classification, clustering, rule induction, pattern recognition, neural networks. Out of these classification and predictions gained much importance as they really promoted intelligent decisions. They have also been introduced in machine learning, statistics and pattern recognition. Although DM techniques have attracted all fields like medical, telecommunication, manufacturing, health care and customer relationship, the technique was not of much attraction to the HR fields. But things have changed recently in HR also or the so called Talent Management(TM) which is considered sometimes within and beyond HRM.
It is very important organizations gain valuable information concerning the performance of the employees or human resources. To achieve this, a data mining system is needed to extract and cluster relevant information about employees performance so as to be able to cluster and easily identify the different data set of employees and their performance.
1.1 Theoretical Background
In organizations, DM goes beyond the exact purpose when it reaches knowledge discovery. Employee retentions and compensations are done based on these patterns developed. Knowledge Management (KM) is about developing, sharing and applying knowledge within organization to gain and sustain a competitive advantage. Nowadays, in the knowledge era (K-Era), knowledge is a valuable asset and among the crucial issues to address. Knowledge can be discovered through many approaches and one of them is by using data mining technique. In data mining, tasks such as classification, clustering and association are used to discover implicit knowledge from huge amount of data. Classification technique is a supervised learning technique in machine learning, which the class level or the target is already known. There are many fields adapted this approach as their problem solver method, such as finance, medical, marketing, stock market, telecommunication, manufacturing, health care, customer relationship, education and some others. Nevertheless, the application of data mining has not attracted much attention in Human Resource Management (HRM) field (Chien & Chen, 2008;Ranjan, 2008). The vast amount of data in HRM can provide a rich resource for knowledge discovery and for decision support system development. Besides that, the valuable knowledge discovered from data mining process should be considered as part of knowledge management issues. In any organization, they have to struggle effectively in term of cost, quality, service or innovation. The success of these tasks depends on having enough right people with the right skills, employed in the appropriate locations at appropriate point of time. This is categorized as part of the talent management task in HRM. In addition, talent management is a process to ensure the right person is in the right job (Cubbingham, 2007).
1.2 Statement of the Problem
Recently, among the challenges of human resource professionals are managing an organization talent which involves a lot of managerial decisions. These types of decision are very uncertain and difficult. It depends on various factors like human experiences, knowledge, preferences and judgments. Besides that, the process to identify the existing talent in an organization is among the top talent management issues and challenges. Employees in an organization are evaluated based on their performance in order to represent their talent ability. In an organization it is difficult to determine the level of performance of employees without a data mining system to extract, classify and predict employee performance. It is in view of this need to apply data mining for obtaining relevant information about employees that necessitated this study.
1.3 Aim and Objectives of the Study
The aim of the research work is to develop an employee data mining information system. The following are the specific objectives:
1.4 Significance of the Study
The significance of the research work is that it will provide relevant information for the local government employers in Oruk Anam to obtain vital information about the performance of employees. It will enable them identify employees that need more training and those that are performing well. The study will serve as an instant information system for the top level management in the local government. The study will also serve as a useful reference material to other researchers seeking for information related to the subject
1.5 Scope of the Study
This research work covers employee data mining information system using Oruk Anam local government area as a case study. It is limited to the mining of employees performance in the local government secretariat.
1.6 Organization of the Research
This research work is organized into five chapters. Chapter one is concerned with the introduction of the research study and it presents the preliminaries, theoretical background, statement of the problem, aim and objectives of the study, significance of the study, scope of the study, organization of the research and definition of terms.
Chapter two focuses on the literature review, the contributions of other scholars on the subject matter is discussed.
Chapter three is concerned with the system analysis and design. It presents the research methodology used in the development of the system, it analyzes the present system to identify the problems and provides information on the advantages and disadvantages of the proposed system. The system design is also presented in this chapter.
Chapter four presents the system implementation and documentation, the choice of programming language, analysis of modules, choice of programming language and system requirements for implementation.
Chapter five focuses on the summary, constraints of the study, conclusion and recommendations are provided in this chapter based on the study carried out.
1.7 Definition of Terms
Data Mining: A technique for searching large-scale database for patterns.
Classification: A distribution into groups
Prediction: To estimate how something will be in future.
