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A method of mining positive and negative sequence rules

A positive sequence and negative sequence technology, applied in the field of mining positive and negative sequence rules, to achieve the effect of improving cross-selling capabilities, increasing trading opportunities, and improving economic benefits

Inactive Publication Date: 2019-01-04
QILU UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical task of the present invention is to provide a method for mining positive and negative sequence rules to solve the problem of how to mine negative sequence rules to fully reflect the relationship between customers' purchasing behavior and purchased goods.

Method used

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  • A method of mining positive and negative sequence rules
  • A method of mining positive and negative sequence rules
  • A method of mining positive and negative sequence rules

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0039] A method of digging positive and negative sequence rules of the present invention can obtain positive sequence rules and negative sequence rules, and both the front key and the back key of the negative sequence rules can contain negative elements, which can reflect the purchase behavior of customers The order of priority can also reflect the relationship between the negative correlation and positive correlation between the commodities in each shopping behavior. The method includes the following steps:

[0040] S100, perform data processing, and convert the customer's transaction data into a sequence database in chronological order, for example, convert the transaction data in the practice database in Table 1 into the sequence database in Table 2;

[0041] Table 1 transaction database

[0042]

[0043] Table 2 Sequence database

[0044] customer ID

customer purchase sequence

1

2

3

4

[0045] S200. Mining...

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Abstract

The invention discloses a method for mining positive and negative sequence rules, belonging to the field of sequence rule mining. The technical problem to be solved is how to mine negative sequence rules to comprehensively reflect the relationship between the purchasing behavior of customers and purchasing commodities. The method comprises the following steps: mining a sequence database to obtaina positive and negative sequence pattern, wherein the positive and negative sequence pattern comprises a positive sequence pattern and a negative sequence pattern; generating positive and negative sequence rules by the positive and negative sequence patterns, wherein the positive and negative sequence rules comprise a positive sequence rule and a negative sequence rule, and both the front key andthe back key of the negative sequence rule can contain negative elements; based on the support degree, correlation and confidence degree of the sequence rules, each of the above sequence rules being selected in turn to obtain the positive and negative sequence rules that meet the user-defined requirements. The method not only reflects the purchase order of goods in the purchase behavior of customers, but also reflects the relationship between the negative correlation and the positive correlation of goods in each purchase behavior.

Description

technical field [0001] The invention relates to the field of sequence rule mining, in particular to a method for mining positive and negative sequence rules. Background technique [0002] The main purpose of the sequence model is to study the sequence of commodity purchases and find out the rules, that is, not only need to know whether the commodity is purchased, but also need to determine the sequence of the commodity and other commodities purchased, for example, the purchase behavior C may be in some purchases Behaviors A and B are frequently followed, but there are also many cases where A and B do not follow C. In this case, predicting that C will happen after AB, based on the sequential pattern ABC, could be a huge mistake. Therefore, in order to make a prediction, one needs to indicate how many times C occurs and how many times C does not occur after the sequential pattern AB. Obviously, the sequential model cannot solve the problem of commodity prediction very well. ...

Claims

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Application Information

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IPC IPC(8): G06Q30/02
CPCG06Q30/0201G06Q30/0202
Inventor 董祥军蒋晓琦
Owner QILU UNIV OF TECH
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