<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the rapid increase of information generated from all kinds of sources, temporal big data mining in business area has been paid more and more attention recently. A novel data mining algorithm for mining temporal association is proposed. Mining temporal association can not only provide better predictability for customer behaviour but also help organisations with better strategies and marketing decisions. To compare the proposed algorithm, two methods to mine temporal association are presented. One is improved based on a traditional mining algorithm, Apriori. The other is based on an Index-Tree. Moreover, the proposed method is extended to mine temporal association in multi-dimensional space. The experimental results show that the Index-Tree method outperforms the Apriori-modified method in all cases.</p>
</abstract>

With the rapid increase of information generated from all kinds of sources, temporal big data mining in business area has been paid more and more attention recently. A novel data mining algorithm for mining temporal association is proposed. Mining temporal association can not only provide better predictability for customer behaviour but also help organisations with better strategies and marketing decisions. To compare the proposed algorithm, two methods to mine temporal association are presented. One is improved based on a traditional mining algorithm, Apriori. The other is based on an Index-Tree. Moreover, the proposed method is extended to mine temporal association in multi-dimensional space. The experimental results show that the Index-Tree method outperforms the Apriori-modified method in all cases.

Temporal association rules discovery algorithm based on improved index tree

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Temporal association rules discovery algorithm based on improved index tree"}

With the rapid increase of information generated from all kinds of sources, temporal big data mining in business area has been paid more and more attention...

ID	Starting	Ending	Support	ID	Starting	Ending	Support
1	2	2	2/5	12	5	6	3/5
2	3	3	1/2	13	6	7	7/10
3	4	4	1/2	14	7	8	3/5
4	5	5	3/5	15	8	9	2/5
5	6	6	4/5	16	4	6	2/5
6	7	7	7/10	17	5	7	1/2
7	8	8	3/5	18	6	8	3/5
8	9	9	2/5	19	7	9	2/5
9	2	3	2/5	20	5	8	2/5
10	3	4	2/5	21	6	9	2/5
11	4	5	2/5

Temporal association rules discovery algorithm based on improved index tree

Published Online: Mar 19, 2021

Page range: 115 - 128

Received: Dec 01, 2020

Accepted: Jan 31, 2021

DOI: https://doi.org/10.2478/amns.2021.1.00016

Keywords
data mining, temporal data mining, association rule, apriori algorithm

© 2020 Chen Yuanyuan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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All frequent intervals and their supports

Temporal association rules discovery algorithm based on improved index tree

Published Online: Mar 19, 2021

Page range: 115 - 128

Received: Dec 01, 2020

Accepted: Jan 31, 2021

DOI: https://doi.org/10.2478/amns.2021.1.00016

Keywordsdata mining, temporal data mining, association rule, apriori algorithm

© 2020 Chen Yuanyuan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

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Fig. 17

All frequent intervals and their supports

Keywords
data mining, temporal data mining, association rule, apriori algorithm