This paper aims at dealing with the practical shortages of nearest neighbor based data mining techniques, especially, clustering and outlier detection. In particular, when there are data sets with arbitrary shaped clusters and varying density, it is difficult to determine the proper parameters without a priori knowledge. To address this issue, we define a novel conception called natural neighbor, which can better reflect the relationship between the elements in a data set than k-nearest neighbor does, and we present a graph called weighted natural neighborhood graph for clustering and outlier detection. Furthermore, the whole process needs no parameter to deal with different data sets. Simulations on both synthetic data and real world data show the effectiveness of our proposed method.
['Qingsheng Zhu', 'Ji Feng', 'Jinlong Huang']
Weighted natural neighborhood graph: an adaptive structure for clustering and outlier detection with no neighborhood parameter