Abstract
The clustering over various granularities for high dimensional data in arbitrary shape is a challenge in data mining. In this paper Nearest Neighbors Absorbed First (NNAF) clustering algorithm is proposed to solve the problem based on the idea that the objects in the same cluster must be near. The main contribution includes: (1) A theorem of searching nearest neighbors (SNN) is proved. Based on it, SNN algorithms are proposed with time complexity O(n*log(n)) or O(n). They are much faster than the traditional searching nearest neighbors algorithm with O(n2). (2)The clustering algorithm of NNAF to process high dimensional data with arbitrary shape is proposed with time complexity O(n). The experiments show that the new algorithms can process efficiently high dimensional data in arbitrary shape with noisy. They can produce clustering over various granularities quickly with little domain knowledge.
Supported by Grant of National Science Foundation of China (60473071), and Specialized Research Fund for Doctoral Program by the Ministry of Education (SRFDP 20020610007).
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Hu, Jj., jie-Tang, C., Peng, J., Li, C., Yuan, Ca., Chen, Al. (2005). A Clustering Algorithm Based Absorbing Nearest Neighbors. In: Fan, W., Wu, Z., Yang, J. (eds) Advances in Web-Age Information Management. WAIM 2005. Lecture Notes in Computer Science, vol 3739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11563952_67
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DOI: https://doi.org/10.1007/11563952_67
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