Uncertain data model oriented utility item set mining method

Abstract

The invention relates to an uncertain data model oriented utility item set mining method. The method comprises the steps of S1, verifying transaction weighting utility and expected support degree of each item set and forming a total candidate set by taking item sets passing the verification as candidate sets; and S2, verifying the utility of each item set in the total candidate set through an uncertain data model to obtain an uncertainty based high-utility item set, wherein the transaction weighting utility is equal to the sum of transaction utilities of all transactions containing the item sets in the uncertain data model, the item sets forming the total candidate set include a k-item set verified through the step S1, k is 1-n, n is an item number of the transaction with the most items in the uncertain data model, and when k is greater than 1, the k-item set is obtained by using a k-1-item set in the total candidate set as a subset and performing verification through the step S1. According to the method, an optimization method is added in an existing mining method, so that the technical effects of reducing resource consumption and shortening running time are achieved.

Description

technical field [0001] The invention relates to a data mining technology for effectively extracting useful information from a large amount of data, in particular to a utility item set mining method for uncertain data models, which can be used for e-commerce platforms Mining of Utility Itemsets in Uncertain Data Model of Packaged Goods Sales. Background technique [0002] The emergence of data mining technology enables people to effectively extract useful information from a large amount of data. The widespread package sales model on the e-commerce platform (many types of commodities represented by toiletries and cosmetics) has gradually drawn attention to the utility itemset mining model and method in uncertain data. By discovering the actual correlation between products and on the premise that the profit reaches a certain standard, the correct package sales model can be formulated. At the same time, we must pay attention to the user's feedback, which is the criterion for w...

AI Technical Summary

Problems solved by technology

[0004] 1. The actual number of various commodities included in the packaged commodities on the platform may not be the same;

[0005] 2. The profits brought by the various commodities actually included in the packaged commodities on the platform are different;

[0024] But when the number of items in the data set increases, for example, when there are 40 items, theoretically there will be 2 itemsets 40 -1 piece, about 1.1×10 12 , although the actual number of itemsets will not reach this number, but the number will still be very large, querying and verifying each itemset separately will occupy and consume a lot of resources, and the running time will be longer

[0025] In this regard, in order to improve efficiency, there are many methods in the prior art, such as the current high-efficiency "IHUP-FPG" hybrid method, that is, FP-Growth (frequent pattern growth) can be used to mine items greater than or equal to the expected support threshold. Set, use the IHUP (Incremental High Utility Pattern) method to mine high-utility item sets, and then take the intersection of the two result sets. Although this method can obtain the target result set with high efficiency, it still has room for improvement.

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