Data preprocessing method based on EM algorithm and KNN algorithm

A KNN algorithm and data preprocessing technology, applied in electrical digital data processing, special data processing applications, digital data information retrieval, etc., can solve problems such as troublesome operation, and achieve the effect of simple operation and high filling accuracy.

Pending Publication Date: 2020-04-21
UESTC COMSYS INFORMATION
View PDF7 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Compared with other missing value filling algorithms, the K-nearest neighbor filling algorithm has the advantages of simple operation and high filling accuracy, but the algorithm needs to manually set the K value, and different training data require different K values. , troublesome operation

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Data preprocessing method based on EM algorithm and KNN algorithm
  • Data preprocessing method based on EM algorithm and KNN algorithm
  • Data preprocessing method based on EM algorithm and KNN algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045]The method proposed by the present invention belongs to the filling method, and the background technology involved in the method is set forth below:

[0046] 1. Expectation-maximization algorithm (EM) algorithm

[0047] The Expectation-maximization algorithm (EM) algorithm is an algorithm for finding the maximum likelihood estimation or maximum a posteriori estimation of parameters in a probabilistic model, where the probability model depends on unobservable hidden variables (Latent Variable). The algorithm mainly calculates through two steps alternately. The first step is to calculate the expectation (E), using the existing estimated value of the hidden variable to calculate its maximum likelihood estimate; the second step is to maximize (M), to maximize The value of the parameter is calculated by the maximum likelihood value obtained on the E step. The parameter estimates found in the M step will be used for the calculation of the next E step, and this process is repe...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a data preprocessing method based on an EM algorithm and a KNN algorithm, and the method comprises the following steps: S1, enabling an original data set to be divided into a complete data subset and an incomplete data subset according to determination of whether an attribute value is missing or not, enabling the complete data subset to serve as the training sample of the EM algorithm, and carrying out the clustering through the EM algorithm; and S2, performing missing value filling on a clustering result by using a KNN algorithm. According to the method, before the KNNis used for missing value filling, the EM algorithm is firstly used for clustering analysis of the original data set, then the KNN is used for missing value filling on the basis of the obtained clustering result, the operation is simple, and the filling accuracy is high.

Description

technical field [0001] The invention belongs to the technical field of data mining, in particular to a data preprocessing method based on EM algorithm and KNN algorithm. Background technique [0002] Financial statement analysis is to process, analyze, compare, evaluate and explain the data provided by the enterprise's financial statements. If it is said that bookkeeping and tabulation belong to the reflection function of accounting, then the analysis of financial statements belongs to the function of interpretation and evaluation. The purpose of financial statement analysis is to judge the financial status of the enterprise and diagnose the gains and losses of the enterprise's operation and management. Through analysis, we can judge whether the financial situation of the enterprise is good, whether the operation and management of the enterprise are sound, and whether the business prospect of the enterprise is bright. There are two main methods of financial statement analy...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & AuthorityApplications(China)
IPC IPC(8): G06K9/62G06F16/215
CPCG06F16/215G06F18/23G06F18/24147G06F18/10
Inventor唐雪飞黄永鑫蒲高飞胡茂秋
OwnerUESTC COMSYS INFORMATION