1. 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
2. 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.
3. 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
4. EXISTING SYSTEM
• The existing system uses various manual methods such as,
• Field review.
• Essay Appraisal
• Forced-choice rating
• Graphic rating scale
• Checklist
• Rating Scales
5. 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.
6. 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.
8. 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
14. SCENARIO BASED ON K-MEAN
1 4.250 63.750
2 0.500 20.000
3 2.000 35.000
15. MODULES
Employee login
Employee index
Questionnaire
Reviewers report generation
Employees Cluster using K-Means
16. 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.
17. 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.
