Application of K-nearest neighbour predictor for classifying trust of B2C customers
K-nearest neighbor (k-NN) classification is one of the most fundamental classification methods and should be one of the first choices for a classification study when there is little or no prior knowledge about the distribution of the data. In addition, nearest neighbor analysis is a method for class...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/46619/ |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | K-nearest neighbor (k-NN) classification is one of the most fundamental classification methods and should be one of the first choices for a classification study when there is little or no prior knowledge about the distribution of the data. In addition, nearest neighbor analysis is a method for classifying cases based on their similarity to other cases. In this paper using k-NN method some factors that affect on customer trust in online transactions, were classified. Raw data gathered from customers when they were buying as customer in B2C websites. One questionnaire was developed and data was gathered from online customers. After organizing data, k-NN method was applied and desired results were obtained. Results showed that in which positions customer can trust to B2C websites and which factors are more significant. Accordingly, in this paper k-NN enable us to predict role of factors on trust level in five levels. |
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