Box separation method based on k-means clustering

Abstract

The invention discloses a box separation method based on k-means clustering. The box separation method comprises the following steps that continuous variables are preprocessed; normalization processing is carried out on the preprocessed data, a k-means clustering algorithm is applied on the data obtained after the normalization processing is carried out to divide the data into a plurality of sections; the equal interval method is adopted for setting the initial center of the k-means clustering algorithm to obtain clustering centers; after the clustering centers are obtained, the midpoint of the adjacent clustering centers is used as a classification division point, each object is added into the closest class, and therefore the data are divided into the multiple sections; each clustering center is calculated again, then the data are divided again until each clustering center does not change any more, and the final clustering result is obtained. According to the box separation method, the technical problem that errors are likely to be caused for a data set with the obvious data density distribution bias according to an existing box separation method is solved, the k-means clustering algorithm does not select the initial center randomly any more, and the data separation result is accurate.

Description

technical field [0001] The invention belongs to the field of data preprocessing of data analysis and mining, in particular to a binning method based on k-means clustering. Background technique [0002] In various data analysis and mining, one of the means of data preprocessing is to discretize continuous variables, and the most commonly used discretization method is to bin continuous variables. An excellent binning method can effectively remove noise of continuous variables, smooth data, increase data granularity, reduce data computational complexity, and provide a better qualitative and quantitative analysis basis for subsequent analysis and mining. [0003] At present, the commonly used binning methods are the equal depth method, the isometric method and the expert definition method. The equal depth method is to sort the data, and each box has the same amount of data; the isometric method is to sort the data set and distribute it evenly in the data value interval, that is...

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Problems solved by technology

[0015] In order to solve the technical problem that the existing binning method is likely to cause errors for data sets with obvious data density distribution bias, the present invention provides a binning method based on the k-means clustering algorithm, using the k-means clustering algorithm to formulate reasonable and accurate The binning method, in which the k-means clustering algorithm is no longer randomly selected when selecting the initial center, and the data binning results are accurate

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