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Association rule mining method 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, etc., and achieve the effects of improving mining efficiency, improving processing efficiency, and good scalability
Inactive Publication Date: 2013-04-03
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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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
...
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 ...
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Abstract
The invention provides an association rule mining method of large-scale data, and the method comprises the following steps that (1) the input data is subjected to classified preprocessing based on similarity, so that records in the same category have high similarity; (2) the data in each category is mined based on Apriori algorithm to obtain frequent item sets of all categories; and (3) the frequent item sets of all the categories are merged, and association rules which correspond to the frequent item sets which are more than the minimum confidence coefficient are determined to be strong association rules. According to the association rule mining method of large-scale data, unnecessary candidate item sets with small association can be reduced, so that the association rule mining efficiency of all the data is improved, and better expandability is realized.
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, connecting multiple data processing nodes into a whole through network connections, thus completing The task of efficiently processing massive amounts of data. [0004] Association rules were proposed by Agrawal et al. in the liter...
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