EMPLOYEE PERFORMANCE APPRAISAL USING

K-MEAN CLUSTERING ALGORITHM

Divya.S -311511205008

Radhika.T -311511205030

Santhosini.K.M -311511205036

Internal Guide -Akila.K M.E

External Guide -Prasanth.K B.Tech

Company Name -IP Rings Ltd

ABSTRACT

• Performance appraisal is used for measuring and evaluating the performance of

the employees in an organisation over a period of time as against a set of

standards.

• 360 degree feedback approach is a feedback taken from various sources.

• Confidentiality encourages employees to give feedback objectively and

constructively.

• Generate an overall report by clustering the employees based on their

performance.

INTRODUCTION

• Appraisal is done with the help of questionnaires containing aspects like

leadership qualities, teamwork, communication, adaptability, goal orientation.

• 360 degree feedback is commonly used for following

For learning & development of the participants.

For supporting the remuneration decisions.

For appraisal, resourcing & succession planning.

• Provide overall analysis of employees performance against their experience

using K-Means clustering

EXISTING SYSTEM

• The existing system uses various manual methods such as,

• Field review.

• Essay Appraisal

• Forced-choice rating

• Graphic rating scale

• Checklist

• Rating Scales

DRAWBACKS

• The feedback approach is one dimensional.

• Manual collection of feedback using 360 degree approach is complex and time

consuming.

• Lacks confidentiality and integrity.

• Identification of the employee performance is difficult.

• Cannot categorize the employees.

• Documentation is difficult.

PROPOSED SYSTEM

• Proposed system uses 360 degree feedback approach.

• Ensures confidentiality of the feedback.

• Employees can be compared with one another in just one category or in total

ranking.

• Employee's recent performance can be compared with his own past rankings.

• Overall employee performance against their experience is used as parameters

for clustering employees.

• Overall clustered report is generated.

KMEANS FLOWCHART

SCENARIO BASED ON K-MEAN • Example of original k-mean clustering in which the centroids are taken

randomly.

EMPLOYEE ATTRIBUTE-1(Experience in years)

ATTRIBUTE-2(Performance in

points)

1 0.5 20

2 1 30

3 2 35

4 3 40

5 3.5 50

6 4 60

7 4.5 70

8 5 75

SCENARIO BASED ON K-MEAN ITERATION-1

EMPLOYEE ATTRIBUTE-1(Experience in years)

ATTRIBUTE-2(Performance in points)

CLUSTER

1 0.5 20 2

2 1 30 1

3 2 35 3

4 3 40 3

5 3.5 50 3

6 4 60 3

7 4.5 70 3

8 5 75 3

SCENARIO BASED ON K-MEAN 1 1.000 30.000

2 0.500 20.000

3 3.667 55.000

SCENARIO BASED ON K-MEAN ITERATION-2

EMPLOYEE ATTRIBUTE-1(Experience in

years)

ATTRIBUTE-2(Performance in

points)

CLUSTER

1 0.5 20 2

2 1 30 1

3 2 35 1

4 3 40 1

5 3.5 50 3

6 4 60 3

7 4.5 70 3

8 5 75 3

SCENARIO BASED ON K-MEAN 1 2.000 35.000

2 0.500 20.000

3 4.250 63.750

SCENARIO BASED ON K-MEAN ITERATION-3

EMPLOYEE ATTRIBUTE-1(Experience in

years)

ATTRIBUTE-2(Performance in

points)

CLUSTER

1 0.5 20 2

2 1 30 3

3 2 35 3

4 3 40 3

5 3.5 50 1

6 4 60 1

7 4.5 70 1

8 5 75 1

SCENARIO BASED ON K-MEAN 1 4.250 63.750

2 0.500 20.000

3 2.000 35.000

MODULES

Employee login

Employee index

Questionnaire

Reviewers report generation

Employees Cluster using K-Means

FUTURE ENHANCEMENTS

• It is computationally very expensive as it involves several distance calculations of

each data point from all the centroids in each iteration.

• The final cluster results heavily depends on the selection of initial centroids

which causes it to converge at local optimum.

• An efficient enhanced k-mean clustering technique can be used. At the next

iteration, we compute the distance to the previous nearest cluster.

• If the new distance is less than or equal to the previous distance, the point stays

in its cluster, and there is no need to compute its distances to the other cluster

centres.

REFERENCES[1] Md. Hedayetul Islam Shovon, MahfuzaHaqua - "An approach of improving students academic performance by using k-means clustering algorithm and decision tree",(IJACSA) International Journal of Advanced Computer Science and Applications, Vol.3, No. 8, 2012.

[2]SavneetKaur - "360 Degrees Performance Appraisal- Benefits & Shortcoming", International Journal of Emerging Research in Management &Technology ISSN: 2278-9359 (Volume-2, Issue-6) june 2013.

[3]M. Espinilla, R. de Andr´es∗, F.J. Mart´ınez, and L. Mart´ınez - "A 360-Degree Performance Appraisal Model Dealing with Heterogeneous Information and Dependent Criteria", December 29, 2011.

[4]S.Ganga, Dr. T.Meyyappan -"Performance of Students Evaluation in Education Sector Using Clustering K-Means Algorithms", International Journal of Computer Science and Mobile Computing, Vol.3 Issue.7, July- 2014.

THANK YOU