A Scalable Approach to Multi-dimensional Data Analysis
Department
Computer Science
Document Type
Article
Publication Date
12-2010
Abstract
Similarity search is one of the most studied research fields in data mining. Given a query data point Q, how to find its closest neighbors efficiently and effectively has always been a challenging research topic. In this paper, we discuss continuous research on data analysis based on our previous work on similarity search problems, and present an approach to improving the scalability of the PanKNN algorithm [13]. 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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