A method and apparatus for data cluster

Abstract

A method and apparatus for data cluster are provided. The method comprises the steps of: in the case of obtaining uncertain data to be clustered, based on the uncertainty probability density functionof the uncertainty data, calculating the information needed for clustering the uncertain data, such as an based on uncertain probability density function of uncertain data, recalculating the preset initial centroid of the dataset, the sum of the expected square error of the recalculated preset initial centroid of the data set and the expected square error of the uncertain data to the preset initial centroid of the other data sets as uncertainty data considered as the sum of the expected square error of the uncertain data with respect to the data set, furthermore, determining the data set withthe minimum sum of expected square error as the target data set, and classifying the uncertain data into the target data set, so as to realize the clustering of the uncertain data by the uncertain probability density function based on the uncertain data, and improve the accuracy of the clustering of the uncertain data.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a data clustering method and device. Background technique [0002] Due to measurement inaccuracy, sampling error, outdated data sources, or other reasons, data often has the nature of uncertainty (referred to as uncertainty data), especially in applications that need to interact with the real environment, such as mobile location services and sensor monitoring In applications such as mobile positioning services, for example, tracking moving targets (such as vehicles or people) in mobile positioning services, it is impossible to completely track the exact instantaneous positions of all moving targets in mobile positioning services, so the position change process of each moving target is accompanied by There is uncertainty, which will affect data management, such as data query and data clustering. [0003] There are two types of uncertainty in current data: exis...

AI Technical Summary

Problems solved by technology

[0004] By using the EM (Expectation Maximization, maximum expectation) algorithm to solve the problem of making the mixed density suitable for uncertainty data clustering and the fuzzy C-means clustering algorithm, but these two data clustering methods do not consider the impact of uncertainty on clustering. influence, resulting in a decrease in clustering accuracy

Images