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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (7): 85-93    DOI: 10.11925/infotech.2096-3467.2018.0999
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Mining Algorithm for Weighted Association Rules Based on Frequency Effective Length
Yong Zhang,Shuqing Li(),Yongshang Cheng
School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210046, China
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Abstract  

[Objective] This paper analyzes the differences in the importance of database items, aiming to address the issues of traditional association mining algorithm with redundant and worthless rules. [Methods] On the sequence with temporal constraints, we explored the non-weighted association rules with the frequency effective length and the weighting methods. Then, we used sliding window technique to study the rare weighted association rules on the time series. [Results] The accuracy of the prediction made by the proposed method increased to 69% from 62%. [Limitations] The mining algorithm took long time to extract the needed rules due to the sliding windows and the large number of rules generated. [Conclusions] The association rules of weighted time series improve the accuracy of recommendation, which also provides new directions for research method on association rules.

Key wordsData Mining      Association Rules      Frequency Length      Sliding Window     
Received: 08 September 2018      Published: 06 September 2019
:  G354  
Corresponding Authors: Shuqing Li     E-mail: leeshuqing@163.com

Cite this article:

Yong Zhang,Shuqing Li,Yongshang Cheng. Mining Algorithm for Weighted Association Rules Based on Frequency Effective Length. Data Analysis and Knowledge Discovery, 2019, 3(7): 85-93.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0999     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I7/85

用户 类型 评分
1 A,C,T,W 3, 1, 5, 0.5
2 C,D,T 1 , 0.5 ,5
3 A,C,W 3 , 1 , 1.5
4 A,C,D,W 4, 1, 2, 1
5 C,D,W 1, 2, 1
类型 权值
A 0.6
C 0.3
D 0.3
T 0.6
W 0.2
用户 类型 计算过程 用户序列平均权值
1 A,C,T,W (0.6+0.3+0.6+0.2)/4 0.43
2 C,D,T (0.3+0.3+0.6)/3 0.4
3 A,C,W (0.6+0.3+0.2)/3 0.37
4 A,C,D,W (0.6+0.3+0.3+0.2)/4 0.35
5 C,D,W (0.3+0.3+0.2)/3 0.27
sum 1.82
频繁集 CT ACW CW
加权支持度(ws) 0.46 0.63 0.78
用户 类型
1 [A,C,T,W,A,W,C,T],W
2 [D,T, T,W,A,W,C,A],W,C
3 [W, T, T,W,A,W,A,T],W,A,C
4 [A,C,D,W,T,A,C,W]
5 [C,D,W]
用户 类型 滑窗中的用户序列平均权值
1 [A,C,T,W,A,W,C,T],W 0.45
2 C,[D,T, T,W,A,W,C,A],W 0.51
3 A,C,[W, T, T,W,A,W,A,T],W 0.56
4 [A,C,D,W,T,A,C,W] 0.38
5 [C,D,W] 0.20
sum 2.10
支持度
置信度
(0.20,0.25] (0.25,0.30] (0.30,0.35] (0.35,0.40]
(0.5,0.6] 0.0378 0.0319 0.0579 0.0287
(0.6,0.7] 0.0469 0.0432 0.0599 0.0433
(0.7,0.8] 0.0748 0.0802 0.0967 0.0830
(0.8,0.9] 0.1020 0.1174 0.0935 0.0667
(0.9,1.0] 0.0462 0.0272 0.0090 0.0066
支持度
置信度
(0.20,0.25] (0.25,0.30] (0.30,0.35] (0.35,0.40]
(0.5,0.6] 0.0031 0.0019 0.0012 0.0009
(0.6,0.7] 0.0091 0.0035 0.0026 0.0018
(0.7,0.8] 0.0190 0.0071 0.0039 0.0024
(0.8,0.9] 0.0543 0.0196 0.0059 0.0025
(0.9,1.0] 0.0826 0.0185 0.0029 0.0011
后续节点位置 命中率 后续节点位置 命中率
1 0.3750 6 0.3701
2 0.3722 7 0.3651
3 0.3725 8 0.3685
4 0.3704 9 0.3695
5 0.3853 10 0.3680
支持度
置信度
(0.20,0.25] (0.25,0.30] (0.30,0.35] (0.35,0.40]
(0.5,0.6] 0.0432 0.0455 0.0554 0.0308
(0.6,0.7] 0.1121 0.1079 0.0560 0.0975
(0.7,0.8] 0.2124 0.1594 0.1142 0.0994
(0.8,0.9] 0.1832 0.1730 0.1270 0.0903
(0.9,1.0] 0.0969 0.0534 0.0172 0.0156
支持度
置信度
(0.20,0.25] (0.25,0.30] (0.30,0.35] (0.35,0.40]
(0.5,0.6] 0.0047 0.0022 0.0018 0.0008
(0.6,0.7] 0.0162 0.0127 0.0049 0.0029
(0.7,0.8] 0.0310 0.0122 0.0065 0.0042
(0.8,0.9] 0.0761 0.0236 0.0097 0.0030
(0.9,1.0] 0.1285 0.0301 0.0054 0.0013
频次有效周期 误差率 频次有效周期 误差率
3 0.3201 8 0.2449
4 0.2995 9 0.2658
5 0.2831 10 0.2576
6 0.2769 11 0.2694
7 0.2769 12 0.2603
区间 准确度 区间 准确度 区间 准确度
(0.10,0.15] 0.69 (0.40,0.45] 0.38 (0.70,0.75] 0.45
(0.15,0.20] 0.55 (0.45,0.50] 0.40 (0.75,0.80] 0.07
(0.20,0.25] 0.53 (0.50,0.55] 0.42 (0.80,0.85] 0.15
(0.25,0.30] 0.49 (0.55,0.60] 0.20 (0.85,0.90] 0.11
(0.30,0.35] 0.52 (0.60,0.65] 0.14 (0.90,0.95] 0.10
(0.35,0.40] 0.43 (0.65,0.70] 0.23 (0.95,1.00] 0.01
区间 覆盖度 区间 覆盖度 区间 覆盖度
(0.10,0.15] 0.40 (0.40,0.45] 0.35 (0.70,0.75] 0.31
(0.15,0.20] 0.41 (0.45,0.50] 0.33 (0.75,0.80] 0.31
(0.20,0.25] 0.36 (0.50,0.55] 0.22 (0.80,0.85] 0.27
(0.25,0.30] 0.38 (0.55,0.60] 0.34 (0.85,0.90] 0.22
(0.30,0.35] 0.37 (0.60,0.65] 0.30 (0.90,0.95] 0.25
(0.35,0.40] 0.36 (0.65,0.70] 0.31 (0.95,1.00] 0.08
最小加权置信度阈值 准确度 最小加权置信度阈值 准确度
0.40 0.69 0.70 0.57
0.50 0.67 0.80 0.53
0.60 0.65 0.90 0.48
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