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User data prediction method and system

A technology for user data and forecasting methods, applied in the field of data analysis

Inactive Publication Date: 2018-07-06
BEIJING KUWO TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

When predicting categorical data and numerical data, the algorithm uses the calculation methods of mode and mean respectively, but these two methods ignore the influence of distance on the target prediction value, that is, the influence of close training samples on unknown samples to be larger than samples farther away

Method used

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  • User data prediction method and system
  • User data prediction method and system

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Embodiment Construction

[0030] The technical solutions of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0031] In the field of data mining, the k-nearest neighbor filling algorithm has the characteristics of simplicity, easy implementation and high filling accuracy. The algorithm uses the calculation methods of mode and mean when predicting categorical data and numerical data respectively. Among them, the mode is the data with the most occurrences. This application optimizes the traditional k-nearest neighbor filling algorithm, thereby optimizing the user profile prediction method.

[0032] figure 1 It is a schematic flowchart of a user profile prediction method provided by an embodiment of the present invention. Such as figure 1 As shown, the method may include:

[0033] Step 110, collect user profile data, and establish a data set, the data set includes a plurality of sample data.

[0034] Step 120, filter the da...

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Abstract

The present invention relates to a user data prediction method and system. The method comprises the steps of: screening a data set, and obtaining training data; according to attribute information of the training data, determining data vectors; in the data vectors, selecting at least one training data being closest to data to be predicted and distance values of the at least one training data and the data to be predicted; and according to the at least one training data and the distance values, obtaining a missing value of the data to be predicted. The prediction method solves the missing value filling problem of classification data and value data, improves the accuracy of the filling missing value and enhances the result interpretability.

Description

technical field [0001] The invention relates to the technical field of data analysis, in particular to a method and system for predicting user data. Background technique [0002] Data missing is an important problem that must be faced in the field of data analysis and processing applications, which directly affects the correctness and accuracy of mining models. Missing filling can provide more information for subsequent processing, play an important role in applications, and become the most effective missing data processing method. Missing filling is the process of inferring or estimating missing data based on observed data. Traditional methods can include regression techniques, nearest neighbor methods, and mean filling. [0003] In the field of data mining, the commonly used missing filling method is k-nearest neighbor filling (k-NN, k-nearest neighbor). The k-NN algorithm is a nonlinear method, which has the characteristics of simplicity, easy implementation and high fi...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/214G06F18/24147
Inventor 赵岩
Owner BEIJING KUWO TECH
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