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Attribute data interval division method and device

A technology of attribute data and data interval, applied in the direction of electrical digital data processing, digital data information retrieval, special data processing application, etc., can solve the problem of low accuracy in dividing attribute data intervals, and achieve the effect of improving accuracy

Active Publication Date: 2015-12-02
ALIBABA GRP HLDG LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0017] The embodiment of the present application provides a method and device for dividing attribute data intervals to solve the problem of low accuracy in dividing attribute data intervals in the prior art

Method used

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  • Attribute data interval division method and device
  • Attribute data interval division method and device
  • Attribute data interval division method and device

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0030] Such as figure 2 As shown, it is a schematic flow chart of the attribute data interval division method in Embodiment 1 of the present application, and the specific processing process is as follows:

[0031] Step 21, extract attribute data of user attributes of several classified members.

[0032] When dividing attribute data intervals offline, the attribute data of user attributes of classified members should be extracted as training data, and the training data should be trained and learned to obtain each attribute data interval.

[0033] Among them, the attribute data of the user attributes of the classified members can be pre-stored in the database. Usually, a record is used to save the attribute data of each user attribute of a member. User attributes can include but are not limited to: age, registration date, gender, Location, source of registration, industry, etc.

[0034] The method of extracting attribute data may be, but not limited to, adopting a method of r...

Embodiment 2

[0069] Corresponding to the attribute data interval division method proposed in Embodiment 1 of the present application, the online processing process of determining the member category of the members to be classified is introduced below.

[0070] Such as image 3 As shown, it is a schematic flow chart of the method for classifying members to be classified online in Embodiment 2 of the present application. The specific processing process is as follows:

[0071] Step 31, in the attribute data of each user attribute of the member to be classified, set the missing attribute data as a preset missing value.

[0072] Among them, the preset missing value should be the same as the preset missing value when dividing the attribute data interval.

[0073] Step 32, for each user attribute of the member to be classified, among the plurality of attribute data intervals corresponding to the user attribute, determine the attribute data interval to which the attribute data of the user attribu...

Embodiment 3

[0081] When dividing the attribute data interval according to the method proposed in Embodiment 1 of the present application, if the number of records contained in the divided attribute data interval is too small, the divided attribute data interval will not have statistical significance. When the data range is used to classify members, the accuracy of the classification is lower. In this regard, Embodiment 3 of the present application proposes an implementation manner for better dividing attribute data intervals.

[0082] Such as Figure 4 As shown, it is a schematic flow chart of the attribute data interval division method in Embodiment 3 of the present application, and the specific processing process is as follows:

[0083] Step 41, extract attribute data of user attributes of several classified members.

[0084] Step 42, for each user attribute of the member, determine each initial attribute data interval corresponding to the user attribute according to the attribute dat...

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Abstract

The present techniques extract attribute data of one or more classified members for one or more user attributes. With respect to a particular user attribute of the one or more user attributes, the present techniques determine initial attribute data intervals corresponding to the particular user attribute based on attribute data and classes of the classified members from the extracted attribute data. With respect to a classified member whose attribute data is missing for the particular user attribute, the present techniques set the attribute data as a preset missing value. The present techniques then merge the preset missing value into each of the initial user attribute data intervals and calculate a Maximum Posteriori Probability (MAP) Bayes estimate value respectively, and determine initial user attribute data intervals with a smallest MAP Bayes estimated value as final attribute data intervals corresponding to the particular user attribute.

Description

technical field [0001] The present application relates to the technical field of member classification processing, in particular to a method and device for dividing attribute data intervals. Background technique [0002] In the prior art, the website generally classifies users into members and non-members according to whether the users are registered in the website. When a member registers on the website, the website will require the member to fill in some user attributes, such as age, registration date, gender, location, registration source, industry, etc. The member identification in the website is correspondingly stored in the database, and usually a record is used to store the attribute data of each user attribute of a member, as shown in Table 1. [0003] Table I [0004] Member ID age registration date gender location Member A 29 November 17, 2011 Female Beijing Member B 36 May 1, 2010 male S...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30G06N20/00
CPCG06N7/005G06F17/30702G06K9/6269G06N99/005G06N20/00G06F16/337G06F18/2411G06N7/01
Inventor 邵纪东
Owner ALIBABA GRP HLDG LTD