Abstract
In this paper, we present continuous research on data analysis based on our previous work on similarity search problems. PanKNN[13] is a novel technique which explores the meaning of K nearest neighbors from a new perspective, redefines the distances between data points and a given query point Q, and efficiently and effectively selects data points which are closest to Q. It can be applied in various data mining fields. In this paper, we present our approach to improving the scalability of the PanKNN algorithm. This proposed approach can assist to improve the performance of existing data analysis technologies, such as data mining approaches in Bioinformatics.
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Shi, Y., Kling, T. (2010). Improving the Ability of Mining for Multi-dimensional Data. In: Zhang, Y., Cuzzocrea, A., Ma, J., Chung, Ki., Arslan, T., Song, X. (eds) Database Theory and Application, Bio-Science and Bio-Technology. BSBT DTA 2010 2010. Communications in Computer and Information Science, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17622-7_30
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DOI: https://doi.org/10.1007/978-3-642-17622-7_30
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