Clustering of faculty by evaluating their appraisal performance by machine learning algorithms

  • Authors

    • Ravinder Ahuja
    • Alisha Chopra
    • Omanshi .
    • Dhruv Sharma
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.15486
  • Clustering, Fuzzy Grouping, Similarities, Unsupervised Algorithms

  • Abstract

    Machine learning is a method which is mainly concerned with the design of the algorithm and with its development. It allows the computer to work according to the given data, mostly in the form of a database; Its major purpose is to automatically make intelligent decisions based on data and to recognize complex patterns. In this paper, we will group the data into multiple clusters on the basis of their similarities and dissimilarities. [5] Clustering is basically dividing the dataset or the given information into the subset (called clusters) so those same properties are classified in the same clusters. In every cluster, observations are similar in some senses. In this research paper, we are considering 15 factors related to the level of their teaching to help evaluate the performance of the staff members. On the basis of the feedback given by the students, the performance level is computed. It helps in assessing the annual increment and other promotion.In this research paper; we divide the staff member into three Group1, Group2, and Group3. Group1 has scored between 25 and 30, Group2 has scored between 20 and 25 and Group3 has scored between 15 and 20. These groups are a divide on the bases of the Points which is the average of all the 15 characteristics.

     

     


     

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  • How to Cite

    Ahuja, R., Chopra, A., ., O., & Sharma, D. (2018). Clustering of faculty by evaluating their appraisal performance by machine learning algorithms. International Journal of Engineering & Technology, 7(2.33), 734-748. https://doi.org/10.14419/ijet.v7i2.33.15486

    Received date: 2018-07-13

    Accepted date: 2018-07-13

    Published date: 2018-06-08