A fast algorithm for mining temporal association rules in a multi-attributed graph sequence

As electronic commerce progresses, temporal association rules are developed by time to offer personalized services for customer’s interests. In this article, we propose a temporal association rule and its discovering algorithm with exponential smoothing filter in a large transaction database. Through experimental results, we confirmed that this is more precise and consumes a shorter running time than existing temporal association rules.

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Temporal association rules for electrical activity detection in residential homes

2016 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata.2016.7840964 ◽

2016 ◽

Cited By ~ 1

Author(s):

Hong-An Cao ◽

Tri Kurniawan Wijaya ◽

Karl Aberer ◽

Nuno Nunes

Keyword(s):

Electrical Activity ◽

Association Rules ◽

Temporal Association ◽

Activity Detection ◽

Residential Homes

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Mining temporal association rules with frequent itemsets tree

Applied Soft Computing ◽

10.1016/j.asoc.2017.09.013 ◽

2018 ◽

Vol 62 ◽

pp. 817-829 ◽

Cited By ~ 23

Author(s):

Ling Wang ◽

Jianyao Meng ◽

Peipei Xu ◽

Kaixiang Peng

Keyword(s):

Association Rules ◽

Frequent Itemsets ◽

Temporal Association

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Visualizing constraint-based temporal association rules

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060401155034 ◽

2001 ◽

Vol 15 (5) ◽

pp. 401-410 ◽

Cited By ~ 3

Author(s):

PANU KORPIPÄÄ

Keyword(s):

Association Rules ◽

Three Dimensional ◽

Temporal Association ◽

Hot Strip Mill ◽

Virtual Model ◽

Rule Mining ◽

Continuous Processes ◽

Hot Strip ◽

Mining Tool ◽

Over Time

When dealing with time continuous processes, the discovered association rules may change significantly over time. This often reflects a change in the process as well. Therefore, two questions arise: What kind of deviation occurs in the association rules over time, and how could these temporal rules be presented efficiently? To address this problem of representation, we propose a method of visualizing temporal association rules in a virtual model with interactive exploration. The presentation form is a three-dimensional correlation matrix, and the visualization methods used are brushing and glyphs. Interactive functions used for displaying rule attributes and exploring temporal rules are implemented by utilizing Virtual Reality Modeling Language v2 mechanisms. Furthermore, to give a direction of rule potential for the user, the rule statistical interestingness is evaluated on the basis of combining weighted characteristics of rule and rule matrix. A constraint-based association rule mining tool which creates the virtual model as an output is presented, including the most relevant experiences from the development of the tool. The applicability of the overall approach has been verified by using the developed tool for data mining on a hot strip mill of a steel plant.

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