An improved density-based outlier mining algorithm;

Algorithm of finding Outlier for reclustering based on distance;

A recognition method of celestial spectra outliers based on concept lattice is proposed by regarding the intension of the concept lattice nodes as characteristic subspace of the celestial spectra.

The data dimension reduction is the main method that can enhance the outliers mining efficiency based on higher-dimension data set.

General clustering approaches,such as distance-based and statistics-based,are not adapted to classification of outliers because of their characteristic of fewness data and sparseness.

In the article described the stray data mining elementary theory as well as the classical algorithm,and according to the characteristics of data in the past dozens years,they made a theory that the use of outlier data mining models to predict diseases and insect pests.

After analyzing the current detection technology,a detection method of the deviated group data based on confident interval is proposed,in which an effective stylebook is screened out from the group data,a credible stylebook is found from the effective stylebook using a genetic arithmetic,a confident interval is obtained based on credible stylebook,then the group data will be detecte.

This paper firstly analyzes outlier and outlier mining algorithms.

In this paper, a research on the outlier mining method for time series data is undertaken.

First,the principle,characteristic and time complexity of three typical outlier mining approaches,that is statistical-based approach, distance-based approach,density-based approach,are analyzed.