3N-Q: Natural Nearest Neighbor with Quality

  •  Shu Zhang    
  •  Malek Mouhoub    
  •  Samira Sadaoui    


In this paper, a novel algorithm for enhancing the performance of classification is proposed. This new method provides rich information for clustering and outlier detection. We call it Natural Nearest Neighbor with Quality (3N-Q). Comparing to K-nearest neighbor and E-nearest neighbor, 3N-Q employs a completely different concept to find the nearest neighbors passively, which can adaptively and automatically get the K value. This value as well as distribution of neighbors and frequency of being neighbors of others offer precious foundation not only in classification but also in clustering and outlier detection. Subsequently, we propose a fitness function that reflects the quality of each training sample, retaining the good ones while eliminating the bad ones according to the quality threshold. From the experiment results we report in this paper, it is observed that 3N-Q is efficient and accurate for solving data mining problems.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1913-8989
  • ISSN(Online): 1913-8997
  • Started: 2008
  • Frequency: quarterly

Journal Metrics

WJCI (2021): 0.557

Impact Factor 2021 (by WJCI):  0.304

h-index (December 2022): 40

i10-index (December 2022): 179

h5-index (December 2022): N/A

h5-median(December 2022): N/A

( The data was calculated based on Google Scholar Citations. Click Here to Learn More. )