Analisis Kinerja Struktur Data Kd-Tree Pada Metode K-Nearest Neighbors

Yuan Lukito

Abstract


K-Nearest Neighbors is a commonly used classification technique that can be categorized into instance-based classification method. The performance of KNN is mostly
determined by the size of the training data. This research compared and analyzed KD-Tree and
Array data structures on KNN implementation. Dataset used in this research has large
multidimensional features. From the experiment conducted we can conclude that KD-Tree data
structure has better and relatively stable performance compared to Array data structure.

Keywords


KD-Tree, K-Nearest Neighbors, Array

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References


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Web: http://java-ml.sourceforge.net/ap i/0.1.7/net/sf/javaml/co re/kdtree/KDTree .html

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Mitchel, T.M. (1997) Machine Learning. Portland: McGraw-Hill.

Skiena, S.S. (2008) The Algorithm Design Manual (2nd Edition). London: Springer-Verlag.


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