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Local scan association rule computer data analysis method based on pre-judging screening
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A data analysis, computer technology, applied in computing, data mining, electrical digital data processing, etc.
Active Publication Date: 2017-02-15
CHINA INFOMRAITON CONSULTING & DESIGNING INST CO LTD
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This algorithm has good scalability and efficiency when processing massive data sets, but the calculation requires strong computing and storage capacity support, usually running in a cluster environment
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Embodiment 1
[0062] Such as Figure 10 Shown, the present invention comprises the steps:
[0063] Step 1, scan the transaction database D to get the set L of frequent k-1 itemsets k-1 ;
[0064] Step 2, the set L of frequent k-1 itemsets k-1 Connect with itself to generate a set of candidate k-itemsets, and the set of candidate frequent k-itemsets is denoted as C K ;
[0065] Step 3, using the Apriori property (all non-empty subsets of any frequent itemset must also be frequent, if a candidate non-empty subset is not frequent, then the candidate must not be frequent) to set C K pruning;
[0066] Step 4, calculate the set C K Pre-judgment support of members in the group, and pre-judgment screening;
[0067] Step 5, perform partial scan judgment on the transactional database D;
[0068] Step 6: Repeat steps 2 to 5 above until no larger frequent itemsets can be found;
[0069] Step 7, the final frequent item set set is recorded as F, then the association rule R={X->Y, X, Y is the fre...
Embodiment 2
[0088] through the pair such as Figure 9 The supermarket sales data set shown (the data set contains 10,000 sales records, that is, 10,000 things, 112 kinds of commodities, that is, 112 items) uses the MAWP algorithm to analyze the association rules, and the performance of the MAWP algorithm is verified. Minimum support min_support=0.05.
[0089] In this example, the frequent itemsets obtained by running the MAWP algorithm and the AWP algorithm and the Apriori algorithm are exactly the same, but the Apriori algorithm needs to scan the data set 967 times, while the MAWP algorithm and the AWP algorithm only need to scan the data set 682 times, compared with the Apriori algorithm. Reduced by 29.47%; the number of transactions scanned by the Apriori algorithm is 9.67×10 6 , the number of transactions scanned by the AWP algorithm is 6.82×10 6 , the number of transactions scanned by the MAWP algorithm is 4.6992×10 5 Compared with Apriori algorithm and AWP algorithm, MAWP reduces...
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Abstract
The invention discloses a local scan association rule computer data analysis method based on pre-judging screening. Aiming at inherent defects of a classic Apriori algorithm, a local scan association rule analysis algorithm-MAWP algorithm based on transaction number query is provided based on an association rule analysis algorithm based on pre-judging screening. The algorithm records transaction numbers including a frequency k item set, and partially scans the transactions in the database but not completely scans the same during a process of verifying the screened candidate k item set, namely only scans a transaction set including the k-1 item set and with the minimal amount of the transactions, so that the total amount of the transactions scanned for determining the frequency item set is decreased, the operation time of the algorithm is reduced, and the operation efficiency of the algorithm is improved.
Description
technical field [0001] The invention belongs to the technical field of computer data mining and information processing, and in particular relates to a computer data analysis method for local scan association rules based on pre-judgment screening. Background technique [0002] Today, with the rapid development of big data technology, people gradually realize that data is wealth, especially the analysis of business data has great practical value. As one of the main methods of data mining, association rule analysis is an indispensable and important part of data mining technology. It is mainly used to discover valuable and interesting connections and rules hidden in large transaction databases. Therefore, the research on association rule algorithms is of great significance. [0003] As early as 1993, IBM's computer scientist R. Agrawal and others discovered the purchase rules of customers when purchasing goods in the customer transaction database, and proposed the correlation m...
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IPC IPC(8): G06F17/30
CPCG06F16/2465G06F2216/03
Inventor 赵学健袁源孙知信乔爱锋陈思光王鹏
Owner CHINA INFOMRAITON CONSULTING & DESIGNING INST CO LTD
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