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User preference based data cleaning method

A technology of data cleaning and user data area, applied in neural learning methods, electrical digital data processing, special data processing applications, etc., can solve problems such as inability to effectively distinguish user-specific data, and achieve integrity and security Effect

Inactive Publication Date: 2010-05-12
UESTC COMSYS INFORMATION
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AI Technical Summary

Problems solved by technology

[0003] In order to overcome the deficiency that existing data cleaning methods cannot effectively distinguish user-specific data, the utility model provides a data cleaning method, which can automatically learn user preferences, thereby identifying user data using optimal data location prediction Data analysis method to efficiently and accurately identify and mark "dirty data"

Method used

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Examples

Experimental program
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Embodiment Construction

[0017] System architecture such as figure 1 as shown.

[0018] 1. User preference learning and labeling system

[0019] The user preference learning and marking system uses the K-means fuzzy clustering analysis neural network algorithm to realize the learning and memory of user behavior, and uses a large amount of user behavior data as sample data for training to complete the identification of user preferences and mark the preference data.

[0020] The K-means algorithm belongs to a kind of cluster analysis, which is to divide a group of physical or abstract objects into several groups according to the degree of similarity between them; among them, similar objects form a group, and this process is called clustering process. That is, searching for valuable connections between data items from a given data set. In many applications, all objects in a cluster are often treated or analyzed as one object:

[0021] (1) Input: the number of clusters k, and a database containing n d...

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Abstract

A user preference based data cleaning method is characterized by adopting a semi-supervised learning algorithm and using a K-means fuzzy clustering analysis method to carry out semantic content marking on the preferred information of users, thereby forming a corresponding user preference data area in a data storage area; meanwhile, using the monitoring service in the user preference data area to monitor the user preference data area in real time, analyzing the change of the data in the data area and predicting the possible results, thereby deciding the operation on the next step. In a data cleaning module, the dirty data recognition service is an important part of data cleaning, and the dirty data are efficiently and accurately recognized and marked by adopting the data analysis method of optimal data location prediction. The data cleaning service gets rid of the dirty data and the error data in the system and inputs the clean data via an external interface of a bottom hardware interface.

Description

Technical field [0001] The invention relates to a data cleaning method based on user preference, especially when there are many user data categories and a huge amount of data. Background technique [0002] Most of the current data cleaning methods are for data cleaning of a certain type of specific application domain. They are centered on "data" in terms of design patterns and usage methods, ignoring the real core of "users". Although these methods can be based on the discovery The error mode of the system, the preparation of programs or the use of external standard source files, data dictionaries and other means to correct errors to a certain extent; however, it is often necessary to compile complex programs or use manual intervention to complete; and this series of work is aimed at a certain Developed for specific industries, not universal. Contents of the invention [0003] In order to overcome the deficiency that existing data cleaning methods cannot effectively disti...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06N3/02G06N3/08
Inventor 唐雪飞佘堃陈科汪海良
Owner UESTC COMSYS INFORMATION
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