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Incremental Association Rule Mining Algorithm Based on Hadoop Cover
By: Zhu Ying and  Wang Jianguo  
Open Access
|Oct 2019

Figures & Tables

Figure 1.

Order three of the B+ tree
Order three of the B+ tree

Figure 2.

Frequent item set generation step diagram
Frequent item set generation step diagram

Figure 3.

HBPT-FUP algorithm step diagram
HBPT-FUP algorithm step diagram

Figure 4.

Apriori and HBPT-FUP algorithms time comparison
Apriori and HBPT-FUP algorithms time comparison

Figure 5.

Comparison of the number of association rules
Comparison of the number of association rules

Figure 6.

Memory usage comparison
Memory usage comparison

Figure 7.

Memory usage comparison
Memory usage comparison

SYMBOLIC DESCRIPTION

symbolmeans
DBraw data set
dbnew data set
DBUdball data sets
Lkfrequent item sets(k-order)
Ckcandidate set(k-order)

VERTICAL DATABASE

ItemTID
butter1,5,6
citrus fruit1,3
coffee2,5
cream cheese4,6
ham1,5,6
newspapers3,6
sauces5,6
tropical fruit2,4
whole milk3,4
yogurt1,2,4,5

DIFFERENCE QUOTIENT TABLE

Number of rules (nk)xkf(xkFirst order difference quotientSecond order difference quotient
10.06250.6  
40.250.4-1.07 
70.43750.3-0.531.44

HORIZONTAL DATABASE

TIDItem
1citrus fruit, yogurt, butter, ham
2tropical fruit, yogurt, coffee
3whole milk, citrus fruit, newspapers
4tropical fruit, yogurt, cream cheese, whole milk
5coffee, butter, yogurt, sauces, ham
6butter, ham, cream cheese, sauces, newspapers

RULE SUPPORT AND CONFIDENCE CALCULATION RESULTS

CD\RDhambreadcoffee
butter(0.4,0.55)(0.13,0.28)(0.2,0.3)
yogurt(0.36,0.54)(0.63,0.58)(0.26,0.39)
cheese(0.43,0.51)(0.3,0.53)(0.31,0.52)
Language: English
Page range: 7 - 16
Published on: Oct 14, 2019
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2019 Zhu Ying, Wang Jianguo, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.