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EMPLOYEE DATA MINING INFORMATION SYSTEM
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:
To develop a database application to capture employee
performance record.
To use data mining technique to extract and process the
information for evaluation of employee performance.
To develop a system that will cluster employee performance
information for easy management of employees.
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.
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