International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 9, No. 3, 8 Mar. 2017
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Clustering of Faculty by Evaluating their Appraisal Performance by using Feed Forward Neural Network Approach
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Author(s)
Index Terms
Clustering, Fuzzy Grouping, Fuzzy partitions, Range of values, Similarities, Neural networks, Hidden layers, feedback
Abstract
Clustering is the process of grouping a set of data objects into multiple groups or clusters with high similarities and dissimilarities. Dissimilarities and Similarities are assessed on the attribute values describing the objects and often involve distance measures. Clustering acts as a data mining tool by having its roots in many application areas such as biology, security, business intelligence, web search etc.
Our Institute is currently using a software application with a name “Merit System”, which evaluates the performance of the staff members regarding their level of teaching by considering various factors. It computes the performance level by collecting feedback from every student. It gives the appraisal result in the form of 30 points earned to every staff member. It acts as a tool for the management of our college to gauge the performance level of the teacher which in turn helps them in assessing annual increments and other promotions.
The main drawback of this system is its inability in grouping of staff members like Group-A, Group-B, Group-C etc. Because, many of the staff members have scored the performance points in the range of 21 to 30 which will creates lot of ambiguities to the management to make clusters of staff members to these groups. This issue is the prime concern of this paper and it was given with an approach to solve this problem by considering possible optimum soft computing technique that includes Feed Forward Neural Network approach.
Cite This Paper
C.Bhanuprakash, Y.S. Nijagunarya, M.A. Jayaram,"Clustering of Faculty by Evaluating their Appraisal Performance by using Feed Forward Neural Network Approach", International Journal of Intelligent Systems and Applications (IJISA), Vol.9, No.3, pp.34-40, 2017. DOI:10.5815/ijisa.2017.03.05
Reference
[1]R.B.V.Subramanyam and A.Goswami. “A Fuzzy Data Mining Algorithm for Incremental Mining of Quantitative Sequential Patterns “International Journal of Uncertainty, Fuzziness and Knowledge-Based systems, Vol-13, No-6, 2005, 633-652
[2]Powers, David M W (2011). “Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation” (PDF). Journal of Machine Learning Technologies 2 (1): 37–63.
[3]Jiabin Deng, JuanLi Hu, Hehua Chi. “An Improved Fuzzy Clustering Method for Text Mining “–2nd International Conference on Network Security, Wireless Communications and Trusted Computing”, 978-0-7695-4011-5/10 @ 2010 IEEE
[4]Timothy C.Havens, James C.Bezdek, Marimuthu Palaniswami. “Fuzzy c-Means Algorithms for Very Large Data “IEEE Transactions on Fuzzy Systems, Vol.20, No.6, December 2012.
[5]Junli Lu, Lizhen Wang, Yaobo Li. “A Fuzzy Clustering Method Based on Domain Knowledge” – 8th ACIS International Conference on Artificial Intelligence. 0-7695-2909-7/07 IEEE
[6]Saeed. R. Aghabozorgi, the Ying Wah. “Using Incremental Fuzzy clustering to Web Usage Mining”, International Conference of Soft Computing and Pattern Recognition, 2009
[7]Omar.Y.Alshamesh, Ismail M.Romi. “Optimal Clustering Algorithms for Data Mining “International Journal Information Engineering and Electronic Business, 2013, vol-2, Pages 17 – 22.
[8]Mohammed Abubakar, Wesam Ashour. “Efficient Data Clustering Algorithms, Improvements over K-means”, IJISA, Vol .5, No.3, February 2013.
[9]Deepali Aneja, Tarun kumar Rawat. “Fuzzy Clustering Algorithms for Efficient Medical Image Segmentation”, IJISA, Vol.5, No.11, October 2013.
[10]Shashank Sharma, Megha Goel, Projhot kumar. ”Performance Comparison of Various Robust Data Clustering Algorithms”, IJISA, Vol.5, No.7, June 2013.
[11]Sunitha Chittineni, Raveendra Babu Bhogapathi. “Determining Contribution of Features in Clustering Multi – Dimensional Data Using Neural Network”, IJITCS, Vol.4, No.10, September 2012.
[12]Raed T. Aldahdooh, Weson Ashour. “Distance based Initialization Method for K-Means Clustering Algorithm”, IJISA, Vol.5, No.2, January 2013.
[13]Suvendu Kanungo, Somya Jaiswal. “A Framework for Mining Coherent Patterns Using Particle Swarm Optimization based Biclustering”, IJISA, Vol.7, No.11, October 2015.
[14]Yugal kumar, G. Sahoo. “A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification”, IJISA, Vol.6, No.6, May 2014.
[15]T.N.Nagabhushana, Y.S. Nijagunaryao.“An Effective Data mining in Symbolic data Using Incremental learning Neural Networks”, Elsevier Science, June 2005.
[16]Saeed Khazaee, Karim Faez. “A Novel Classification Method Using Hybridization of Fuzzy Clustering and Neural Networks for Intrusion Detection”, IJMECS Vol. 6, No. 11, November 2014.