An Approach to Nearest Neighboring Search for Multi-Dimensional Data
Finding nearest neighbors in large multi-dimensional data has always been one of the research interests in data mining field. In this paper, the authors present their continuous research on similarity search problems. Previously they have worked on exploring the meaning of K nearest neighbors from a new perspective in PanKNN. It redefines the distances between data points and a given query point Q, efficiently and effectively selecting data points which are closest to Q. It can be applied in various data mining fields. A large amount of real data sets have irrelevant or obstacle information which greatly affects the effectiveness and efficiency of finding nearest neighbors for a given query data point.