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A New Method to Construct the KD Tree Based on Presorted Results

Author

Listed:
  • Yu Cao
  • Huizan Wang
  • Wenjing Zhao
  • Boheng Duan
  • Xiaojiang Zhang

Abstract

Searching is one of the most fundamental operations in many complex systems. However, the complexity of the search process would increase dramatically in high-dimensional space. K-dimensional (KD) tree, as a classical data structure, has been widely used in high-dimensional vital data search. However, at present, common methods proposed for KD tree construction are either unstable or time-consuming. This paper proposed a new algorithm to construct a balanced KD tree based on presorted results. Compared with previous similar method, the new algorithm could reduce the complexity of the construction process (excluding the presorting process) from O (KNlog 2 N) level to O (Nlog 2 N) level, where K is the number of dimensions and N is the number of data. In addition, with the help of presorted results, the performance of the new method is no longer subject to the initial conditions, which expands the application scope of KD tree.

Suggested Citation

  • Yu Cao & Huizan Wang & Wenjing Zhao & Boheng Duan & Xiaojiang Zhang, 2020. "A New Method to Construct the KD Tree Based on Presorted Results," Complexity, Hindawi, vol. 2020, pages 1-7, December.
    • Handle: RePEc:hin:complx:8883945
    DOI: 10.1155/2020/8883945
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