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A Method for Mining Association Rules of Large-Scale Data
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A large-scale data and rule technology, applied in the field of distributed computing and data mining, can solve the problems of long data mining operation time, achieve the effect of improving mining efficiency, good scalability, and meeting user needs
Inactive Publication Date: 2016-04-20
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology
A large number of candidate item sets will be generated during the implementation of the Apriori algorithm, resulting in long data mining operations, which is a major shortcoming based on the Apriori algorithm.
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example 1
[0030] The input data table shown in Table 1 has 9 records (T1, T2, ..., T9) and the items contained in each record (I1, I2, I3, I4, I5):
[0033] In order to facilitate the calculation of the similarity between items in the data, the input data table is converted into a 0,1 state table, as shown in Table 2, 0 means that the current item does not appear in the corresponding record, and 1 means that the current item appears in the corresponding record middle:
[0034] Table 20,1 State table
[0035]
I1
I2
I3
I4
I5
T1
1
1
0
0
1
T2
0
1
0
1
0
T3
0
1
1
0
0
T4
1
1
0 ...
example 2
[0069] Taking frequent itemset mining for a category (T2, T8) as an example, the default minimum support is 0.22.
[0070] The 0,1 state tables of records T2 and T8 are shown in Table 5:
[0071] Table 5 state table
[0072]
I1
I2
I3
I4
I5
T2
1
1
0
0
1
[0073] T8
0
1
0
1
0
[0074] In the first scan, the items contained in this category (I1, I2, I4, I5) are used as candidate item sets alone, and the corresponding support is greater than the minimum support of 0.22 as shown in Table 6:
[0075] Table 6 Support degree of the first scan
[0076]
Support
I1
50%
I2
1
I4
50%
I5
50%
[0077] The frequent 1-itemsets generated by the first scan are: I1, I2, I4, I5
[0078] In the second scan, 2 candidate item sets (I1, I2, I1, I4, I1, I5, I2, I4, I2, I5, I4, I5) including frequent 1-itemsets are generated, and the corresp...
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Abstract
The present invention provides a large-scale data associationrule mining method, which includes the following steps: 1) performing similarity-based classification preprocessing on input data, so that records in the same classification have high similarity; 2) each classification The data in is mined based on the Apriori algorithm to obtain the frequent itemsets of each category; 3) Merge the frequent itemsets of all categories, and determine the association rules corresponding to the frequent itemsets greater than the minimum confidence as strong association rules. The invention can reduce unnecessary candidate item sets with small correlation, thereby improving the mining efficiency of the association rules of the overall data and having better expansibility.
Description
technical field [0001] The invention relates to distributed computing and data mining technology. Background technique [0002] Research on massive data management is not a new topic, but the definition of "massive" is constantly changing with the rapid development of storage devices. [0003] For large-scale data, the databasemanagement system indexes the data through Hash, B+'Iree and other means, which can effectively reduce the cost of reading and writing external memory and improve the efficiency of data query. In order to process a larger amount of data, Parallel DatabaseSystem (Parallel DatabaseSystem, referred to as PDBS) and Distributed DatabaseSystem (Distributed Database System, referred to as DDBS) have emerged one after another. Multiple data processing nodes are connected through the network to form a whole, so as to complete the effective processing of massive data. task. [0004] Association rules were proposed by Agrawal et al. in the literature in 199...
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