Objective: In many organizations, employee data have to be maintained and utilized for many purposes. Here, in this paper, we are going to use such data to calculate an employee's performance.Methods: This employee data may be converted into useful information using data mining techniques such as K-means and decisions tree. K-means is used to find the rank of the employee means that the employee may come under in his criteria. Decision tree is used to find the review of an employee means that the employee needs improvement or he/she meets expectation.Results: This algorithm when utilized can identify the top employee who can be considered for appraisal or the eligible candidates for promotion. Hence, these algorithms such as K-mean and decision tree that help to find best employees for any association and help us to take a good decision in less time.Conclusion: There are various factors which should be considered and are limited to this algorithm, so human intervention is required to consider those factors. However, ranking and appraisal are seen in many companies, and this algorithm will definitely identify the potential candidates

A, Muralidhar

J, Sharath Kumar

Modi, Ashish

English

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Advances in Smart Computing and BioinformaticsSpecial Issue (April)Online - 2455-3891 Print - 0974-2441© 2017 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4. 0/) DOI: http://dx.doi.org/10.22159/ajpcr.2017.v10s1.1069EMPLOYEE PERFORMANCE APPRAISAL SYSTEM BASED ON RANKING AND REVIEWSABSTRACTObjective: In many organizations, employee data have to be maintained and utilized for many purposes. Here, in this paper, we are going to use such data to calculate an employee’s performance.Methods: This employee data may be converted into useful information using data mining techniques such as K-means and decisions tree. K-means is used to find the rank of the employee means that the employee may come under in his criteria. Decision tree is used to find the review of an employee means that the employee needs improvement or he/she meets expectation.Results: This algorithm when utilized can identify the top employee who can be considered for appraisal or the eligible candidates for promotion. Hence, these algorithms such as K-mean and decision tree that help to find best employees for any association and help us to take a good decision in less time.Conclusion: There are various factors which should be considered and are limited to this algorithm, so human intervention is required to consider those factors. However, ranking and appraisal are seen in many companies, and this algorithm will definitely identify the potential candidates.Keywords: Employee performance, Clustering, Decision tree, K-means, Employee performance, Data mining, Euclidean distance.INTRODUCTIONIndustrial sector has a collection of employee data. This data may be converted into useful information for taking a good decision. In every industrial company, there are number of employee data such as position, salary, and attendance. These data may be quality wise analyzed using some mining technique. The performance of employee shows a very important role to get a good job in any company. Performance plays a role on the basis of working experience of the employee. The very important element that is used to evaluate an employee performance in a company is his position and his experience. The other elements such as salary, projects, knowledge, and skills also play an important role to employment opportunity to an employee. Employee should not evaluate only on his experience and position that he achieved. Hence, a grouping of employees is based on the performance of the employee. The research has been based on k-mean clustering and decision tree algorithm for categorizing employees in different clusters. Here, k-mean is used to find the cluster of the employee, and this may help us to find the rank of the employee means either it comes under criteria or not. Then, decision tree is used here by the purpose of finding the review of the employee. Decision tree helps us to find the result of the predefined dataset means, and it gives us to feedback or review of the employee.LITERATURE SURVEYThe paper describes about Silhouette method in K-mean clustering. Silhouette refers to a method of clarification and justification within clusters of data [1,2]. This methodology provides a graphical representation, how all values lie within the cluster. K-mean algorithm splits the number of observation into K clusters [3], where each observation can belong to the cluster with the nearest mean [4]. To use K, silhouette measures are used. The cluster can be ranked or classified to get an enhanced student performance [5,6]. This algorithm may help to get a student performance result in a ranking manner [7].The paper deals with decision tree algorithm [8]. Algorithm classifies map data into predefined group of class means data that may be PROPOSED WORKEuclidean distance X H X WH 12W 1= − + −( ) ( )2WhereXH: Observation value of variable heightH1: Centroid value of cluster 1 for variable heightXW: Observation value of variable weightW1: Centroid value of cluster 1 for variable weightClustering technique (decision tree)Decision tree is a way that is used as a hierarchy such as a map of choice and available issues, along with their results, ability amount, and convenience. This is the root, which we are using to show this approach.To create a decision tree, used these following rules:•	 Choose	one	 instance	of	practice	example	divide	 it	 into	available	instances so that the practice example is scattered in some small-small sets.Department of , School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.  Email: vijayalakshmi.av@vit.ac.in  Received: 23 January 2017, Revised and Accepted: 03 March 2017ASHISH MODI, SHARATH KUMAR J, MURALIDHAR Aclassified  according  to  their  gain  entropy  [8,9].  Then,  higher weighted class goes on the root and then just lower than higher class  further  classified  into  another  class  [10,11].  When  the classification  comes  to  the  last  class,  then  it  divided  into  single value  that  is  the  feedback  of  employee  [12].  The  result  of  the employee is based on employee’s performance.Clustering technique (K-means)Cluster research or clustering is the way of group a set of entities in  which  those  entities  in  similar  group  (cluster)  are  more  same (similar)  to  each  other  than  other  clusters  (groups).  Euclidean distance is also used to find the clusters of the K-means algorithm. To  find  Euclidean  distance  between  observations,  first  take initial  cluster  centroid,  cluster  1  and  cluster  2  make  them  to centroid and calculate. Based on the calculation (Euclidean distance), every  observation  assigns  to  one  cluster  -  calculating  minimum distance.Full Proceeding PaperSpecial Issue (April) Modi et al. •	 After	this	method	is	applied	for	all	instance	of	practice	example.	The	knob of entire practice example refers to equal hierarchy, and no left instance will be done for another dividation then break diving the hierarchy.Decision tree is further classified into two parts or nodes as follows:•	 Leaf	node	-	it	shows	the	appraisal	of	the	instance.•	 Decision	node	-	it	defines	many	analysis	for	single	instance	appraisal	distinct branch with one decision tree for every available outcome of the analysis.For develop tree, we want to analysis the info of every instance. Choose an instance with very high value.Consider two classes X and Y•	 Take	the	sample	Z	which	carry	X	values	of	class	X	and	Y	values	of	class Y.•	 For	finding	that,	Z	is	depend	to	X	and	Y	is	consider	as	follows:I(X,Y)=XX+Y*logXX+Y-YX+Y*logYX+Y2 2Let	that	with	the	help	of	instance	V,	Z	can	be	divided	into	{Z1,Z2,Z3,Z4………Zn}Suppose,	Zi holds Xi of X and Yi of Y, the conventional info wants to divide the attribute in the hierarchy is as follows:E(V)=X +YX+YI(X ,Y )i ii i∑ ( ) *i = 1EntropyConventional	info	wants	to	initialize	a	random	class	attribute	in	Z	in	the	minimal, least-path code.Measure info gained, that is, gain (V). Calculate the dividation of entropy gained by the hierarchy. Select the higher dividation hierarchy.Gain(V) = I(X,Y)-E(V)To find the distance in k-means algorithm, we use Euclidean distance. Euclidean distance between observation and cluster centroid 1 and 2 is calculated. After calculating Euclidean distance, each observation is assigned to 1 of the cluster based on its minimum distance.PERFORMANCE ANALYSISK-means (Euclidean distance)( ) ( ) ( )35000 35000 26 26 5 5 02 2 2− + − + − =( ) ( ) ( ) .35000 47000 26 25 5 3 000 0002 2 2− + − + − =12 21( ) ( ) ( ) .35000 52000 26 28 5 3 000 0002 2 2− + − + − =17 24( ) ( ) ( ) .35000 28000 26 32 5 8 000 002 2 2− + − + − =7 3214( ) ( ) ( ) .35000 50000 26 23 5 2 000 0002 2 2− + − + − =15 6( ) ( ) ( ) .47000 35000 25 26 3 5 000 0002 2 2− + − + − =12 21( ) ( ) ( )47000 47000 25 25 3 3 02 2 2− + − + − =( ) ( ) ( ) .47000 52000 25 28 3 3 000 0002 2 2− + − + − =5 9( ) ( ) ( ) .47000 28000 25 32 3 8 000 002 2 2− + − + − =19 195( ) ( ) ( ) .47000 50000 25 23 3 2 000 0002 2 2− + − + − =3 833Apply the formula of Euclidean distance in Table 1 to find the value of the each data set. Then, we get values for dataset.In Table 2, after getting the values of dataset, then assign each row to a cluster according to their values. Hence, in previous to find cluster through K-means using Euclidean distance technique, we first considered two centroids then calculate the cluster value of first two rows. Then, after this, we update the cluster again, and again, by taking the mean of the centroid. However, in the modified algorithm, there is no need to update centroid again, and again, fix the value of centroid then calculate the values of the cluster. It remains same, but it decreases the calculation time. If column one gets the highest values, then assigns that cluster value of that row is 1 otherwise gives it to cluster 2.Table 3 shows the assignment of the cluster with attributes that show the rank of employees.for i = 1…n for i = 1…n  s = s+([aij]-[a1j])2  t = t+([aij]-[a2j])2p = si1p = si2If pi1>pi2 then assignment = 1else assignment = 2I X Y bi, * log * log .( )= −  − =7107103103100 88122 2 tsTable 1: Dataset collectedId Salary Age Experience1 35000 26 52 47000 25 33 52000 28 34 28000 32 85 50000 23 2Table 2: Clustered dataId Cluster 1 Cluster 2 Assignment1 0 12000.00021 22 12000.00021 0 13 17000.00024 5000.0009 14 7000.003214 19000.00195 25 15000.0006 3000.000833 1Table 3: Assign cluster to datasetId Salary Age Experience Assignment1 35000 26 5 22 47000 25 3 13 52000 28 3 14 28000 32 8 25 50000 23 2 1Decision treeTable  4  contains  the  employee’s  dataset  for  decision  tree.  The result of “proper” is 7 and result of “improper” is 3, there are two different values. Hence, these two values, Class X = 8 and Y = 2, calculate the expectation info and info entropy.496Special Issue (April) Modi et al. E work task =510*I 4,1 +510*I 3,2 =0.8464 bits( ) ( ) ( )E quality  =510*I 2,3 =0.4885 bits( ) ( )E skills =710*I 5,2 +310*I 2,1 =0.7810bits( ) ( ) ( )E inventiveness  610*I 5,1 +410*I 2,2 =0.87074 bits( ) = ( ) ( )Hence, calculate info gain:Gain (work task) = 0.8812-0.8464 = 0.0348 bitsGain (quality) = 0.8812-0.7812 = 0.1 bitsGain (initiative) = 0.8812-0.87074 = 0.0104 bitsThe maximum gain information is quality so select quality as root.Then, split the tree into just minimum gain of quality and then do where every node is node split.  It shows in Fig. 1.Decision tree is also creating by if else command in any programing language.IF (quality = “great” and skills = “genuine” and task = “weighty” and inventiveness = “excellent”), then result = “proper”ELSE IF (quality = “great” and skills = “genuine” and task = “light” and inventiveness = “normal”), then result = “proper”ELSE IF (quality = “great” and skills = “simple” and task = “weighty” and inventiveness = “normal”), then result = “proper”ELSE IF (quality = “poor” and skills = “genuine” and task = “light” and inventiveness = “excellent”), then result = “improper”ELSE IF (quality = “poor” and skills = “simple” and task = “weighty” and inventiveness = “normal”), then result = “improper”CONCLUSIONWe have presented in this work, a new approach for the K-mean and decision tree, that helps to find best employees for any association. A present performance analysis is required to recommendation for excellence salary changes and grade or in-grade change salary increase. This may also help the manager to check the employee performance and those employees want extra attention for decreasing falling ratio for taking the strict action right time. Decision tree method is used on the previous year data of employee performance. This data mining approach helps the institutes, companies, and anywhere, where the need of employee and this ranking and review technique will help to the manager to find best employees in minimal time when he has so many number of employee data or big dataset. Hence, here, we describe about raking and reviews that help us to take a good decision in less time.REFERENCES 1. Kumar SA, Vijayalakshmi MN. Mining of student academic evaluation records in higher education. In: Recent Advances in Computing and Software Systems (RACSS), 2012 International Conference on IEEE; 2012. p. 67-70.2. Geng X, Luo L. Multilabel ranking with inconsistent rankers. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2014. p. 3742-7.3. Bouhmala N. How good is the Euclidean distance metric for the clustering problem. In: Advanced Applied Informatics (IIAI-AAI), 2016 5th IIAI International Congress on IEEE; 2016. p. 312-5.4. Esteves RM, Hacker T, Rong C. Competitive k-means, a new accurate and distributed k-means algorithm for large datasets. In: Cloud Computing Technology and Science (Cloud Com), 2013 IEEE 5th International Conference on IEEE. Vol. 1; 2013. p. 17-24.5. Kumar KM, Reddy AR. A fast K-means clustering using prototypes for initial cluster center selection. In: Intelligent Systems and Control (ISCO), 2015 IEEE 9th International Conference on IEEE; 2015. p. 1-4.6.	 Poteraş	 CM,	 Mocanu	 ML.	 Evaluation	 of	 an	 optimized	 K-means	algorithm based on real data. In: Computer Science and Information Systems (Fed CSIS), 2016 Federated Conference on IEEE; 2016. p. 831-5.Table 4: Dataset for decision treeWork task Skills Inventiveness Quality ResultWeighty Genuine Excellent Great ProperWeighty Simple Excellent Great ProperWeighty Genuine Normal Great ProperWeighty Genuine Normal Great ProperWeighty Genuine Normal Poor ImproperLight Genuine Excellent Poor ImproperLight Genuine Excellent Poor ProperLight Simple Excellent Great ProperLight Genuine Excellent Poor ProperLight Simple Normal Poor ImproperFig. 1: Decision tree497Special Issue (April) Modi et al. 7. Kotalwar R, Gandhi S, Chavan R. Data mining: Evaluating performance of employee’s using classification algorithm based on decision tree. Eng Sci Technol Int J 2014;4:29-35.8.	 Yang	 Y,	 Chen	 W.	 Taiga:	 Performance	 optimization	 of	 the	 C4.	5 decision tree construction algorithm. Tsinghua Sci Technol 2016;21(4):415-25.9. Guleria P, Thakur N, Sood M. Predicting student performance using decision tree classifiers and information gain. In: Parallel, Distributed and Grid Computing (PDGC), 2014 International Conference on IEEE; 2014. p. 126-9.10.	 Vaidya	J,	Shafiq	B,	Fan	W,	Mehmood	D,	Lorenzi	D.	A	random	decision	tree framework for privacy-preserving data mining. IEEE Trans Dependable Secure Comput 2014;11(5):399-411.11. Lin C, Du X, Jiang X, Wang D. An efficient and effective performance estimation method for DSE. In: VLSI Design, Automation and Test (VLSI-DAT), 2016 International Symposium on IEEE; 2016. pp. 1-4.12. Chen Q, Gong Z. Data mining modelling of employee engagement for it enterprises based on decision tree algorithm. In: Information Management, Innovation Management and Industrial Engineering (ICIII), 2013 6th International Conference on IEEE. Vol. 2; 2013. p. 305-8.498

EMPLOYEE PERFORMANCE APPRAISAL SYSTEM BASED ON RANKING AND REVIEWS

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EMPLOYEE PERFORMANCE APPRAISAL SYSTEM BASED ON RANKING AND REVIEWS

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