An Unbiased Candidate Selection using Clustering and Analytic Hierarchy Process
| Autor: | Anuradha .T, Lakshmi Surekha T, Sita Kumari. K |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 0211 other engineering and technologies k-means clustering Analytic hierarchy process 02 engineering and technology Machine learning computer.software_genre Task (project management) Ranking 021105 building & construction 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Artificial intelligence Cluster analysis business computer Categorical variable Selection (genetic algorithm) |
| Zdroj: | 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). |
| DOI: | 10.1109/icecct.2019.8868981 |
| Popis: | Decision making in selecting the best one among different competing alternatives is a crucial task for Humans in many situations. This selection process becomes a complex decision making task when there are many multi criteria objects and each object seems to be equally preferable for selection. AHP is a mathematical model for giving ranking to the objects based on matrix algebra and pair wise comparisons between different numerical and categorical criteria. CAA analysis assigns a value to the student based on regularity, overall performance in academic and nonacademic activities. K means clustering divides the students into homogeneous groups. This paper proposes a combination of CAA analysis, k-means clustering and AHP to find the optimal and unbiased solution for the problem of selecting best student of the department. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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