Approximate Nearest Neighbour Using Data Mining

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Deepika Verma
Namita Kakkar


Data mining may be viewed as the extraction of the hidden predictive information from large databases, is a powerful new technology with great potential to analyze important information in the data warehouse. Nearest neighbor search (NNS), also known as proximity search, similarity search or closest point search, is an optimization problem for finding closest points in metric spaces. This paper presents an extensive study of existing techniques of the approximate nearest neighbour in data mining and a new algorithm is proposed for nearest neighbour. In this paper, we studied and compared k-d tree algorithm and brute force algorithm on various levels. The major contribution achieved by this research is the detection of flaws in both k-d tree and brute-force algorithms which helps to propose a new algorithm.

Keywords: Data Mining, Nearest neighbour, Approximate K-NN, K-d tree, Brute-force.


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