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An Analysis Method of Customer Purchase Behavior Based on Negative Association Rule Pruning Technology of Logical Reasoning

A technology of negative association, positive and negative association, applied in the field of customer purchase behavior analysis based on negative association rule pruning technology, can solve the problems that do not involve redundant negative association rule pruning, redundant positive and negative association rules in rule sets, etc.

Active Publication Date: 2020-10-09
山东元竞信息科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These algorithms can mine both positive and negative association rules, but there is an obvious shortcoming in the analysis of shopping basket data: there are a large number of redundant positive and negative association rules in the mining rule set.
But these algorithms only consider the pruning of redundant positive association rules, but not the pruning of redundant negative association rules

Method used

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  • An Analysis Method of Customer Purchase Behavior Based on Negative Association Rule Pruning Technology of Logical Reasoning
  • An Analysis Method of Customer Purchase Behavior Based on Negative Association Rule Pruning Technology of Logical Reasoning
  • An Analysis Method of Customer Purchase Behavior Based on Negative Association Rule Pruning Technology of Logical Reasoning

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Embodiment 1

[0083] A method for analyzing customer purchase behavior based on negative association rule pruning technology, comprising steps:

[0084] 1) Define association rules

[0085] I={i 1 ,i 2 ,...,i m} is a set of m different items (commodities), i k (k=1,2,...,m) are called items (commodities);

[0086] A transactional database D is a collection of transactions T, whose transaction number is denoted |D|, where T is a collection of items, and There is a unique identifier corresponding to each transaction, which is recorded as TID;

[0087] Let X be a set of items (itemsets) in I, if Then the transaction T is said to contain X; if the number of items contained in X is k (1≤k≤m), then X is called a k-itemset;

[0088] Shaped like The implication-type positive association rule, And X∩Y=Φ, where X is called the former term of the rule, and Y is called the latter term of the rule; the association rule is used to judge that a customer who has purchased product X in the shopp...

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Abstract

The invention discloses a customer purchasing behavior analysis method based on a logical inference negative association rule pruning technology, which belongs to the field of association rule analysis. The method provided by the invention comprises the steps that a frequent itemset satisfying the minimum support threshold is excavated in an object database through an Apriori algorithm; positive and negative association rules are excavated in the frequent itemset by using the related knowledge of a probability theory and a set theory; and redundant positive and negative association rules are pruned through a logical reasoning step to acquire non-redundant positive and negative association rules. The excavated decision association rules can be used to analyze the purchasing behavior of a customer and the relationship between goods, so that a merchant can recommend goods most likely to be purchased for the customer according to the current purchases and sales of goods. The shopping timeof the customer is saved. Future purchases and sales of goods can be predicted. The placement of goods can be better arranged. The sales of goods can be improved.

Description

technical field [0001] The invention relates to the field of association rule analysis in data mining, in particular to a customer purchase behavior analysis method based on negative association rule pruning technology. Background technique [0002] With the continuous development of electronic information technology, online shopping has become an indispensable part of people's daily life. Compared with traditional offline shopping, the Internet provides a new interactive shopping channel, and consumers get huge advantages: rich product information, overcoming geographical and time barriers, obtaining competitively priced products, and product personality Personalization, customization, more product choices, greater shopping convenience, etc. Through online shopping, people can buy the items and services they want without leaving home, so the data of online shopping has exploded in recent years. At the same time, many large e-commerce websites, such as Amazon, Alibaba’s Ta...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/06G06N5/04
Inventor 董祥军郝峰
Owner 山东元竞信息科技有限公司
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