In this paper,we targeted at high-dimensional datasets and presented Two-Phase Approximate KNN Outliers Mining(TPOM),an algorithm of two-phase based approximate KNN outliers mining.

This paper presents a approximate proximal algorithm finding the zero of a maximal monotone operator in Hilbert space,whose error criterion is weaker than that in the literatures.

Based on the K nearest neighbor algorithm,an improved method was proposed for selecting genes-related documents from biology literature,and then automatically annotating and classifying.

It is a deterministic algorithm, with its time complexity the same as the nearest neighbor algorithm, that is, O(n2), where n is the number of cities.

The decline of recommending quantity resulted in the extreme sparsity of user data was dealt with,based on nearest neighbor algorithm and the combination of selective sampling and lookahead framework for recommendation system,with the purpose of improving the accuracy of recommendation.

This algorithm is improved on the basis of the nearest-neighbor algorithm, and is turned from the partial optimization into the overall optimization, that is turning minimum distance issue into minimum coefficient of average distance issue.

A nearest-neighbor algorithm was used to determine the water usage network including regeneration units.

The relation of vector and point in space could show the relation of the fuzzy image and the original image,contacting nearest-neighbor algorithm to use the method in computational optical section microscopy(COSM).