Abstract
K-Nearest Neighbor based regression algorithm assigns a value to the query instance based on the values of its neighborhood instances. Although kNN has proved to be a ubiquitous classification/regre-ssion tool with good scalability but it suffers from some drawbacks. One of its biggest drawback is that, it is a lazy learner i.e. it uses all the training data at runtime. In this paper, we propose a novel, efficient and accurate, clustering based kNN regression algorithm CLUEKR having the advantage of low computational complexity. Instead of searching for nearest neighbors directly in the entire dataset, we first hierarchically cluster the data and then find the cluster in which the query point should lie. Our empirical experiments with several real world datasets show that our algorithm reduces the search space for kNN significantly and is yet accurate.
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Dubey, H., Pudi, V. (2013). CLUEKR : CLUstering Based Efficient kNN Regression. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_37
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DOI: https://doi.org/10.1007/978-3-642-37453-1_37
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