Mining Healthcare Data with Temporal Association Rules: Improvements and Assessment for a Practical Use

2009 ◽  
pp. 16-25 ◽  
Author(s):  
Stefano Concaro ◽  
Lucia Sacchi ◽  
Carlo Cerra ◽  
Pietro Fratino ◽  
Riccardo Bellazzi
Keyword(s):  
Association Rules ◽  
Temporal Association ◽  
Healthcare Data

Fast Algorithms for Temporal Association Rules in a Large Database

2005 ◽  
Vol 277-279 ◽  
pp. 287-292 ◽  
Author(s):  
Lu Na Byon ◽  
Jeong Hye Han
Keyword(s):  
Electronic Commerce ◽  
Association Rules ◽  
Association Rule ◽  
Temporal Association ◽  
Large Database ◽  
Running Time ◽  
Smoothing Filter ◽  
Personalized Services ◽  
Temporal Association Rule ◽  
Transaction Database

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.

Mining temporal association rules with frequent itemsets tree

2018 ◽  
Vol 62 ◽  
pp. 817-829 ◽  
Author(s):  
Ling Wang ◽  
Jianyao Meng ◽  
Peipei Xu ◽  
Kaixiang Peng
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Temporal Association

Visualizing constraint-based temporal association rules

2001 ◽  
Vol 15 (5) ◽  
pp. 401-410 ◽  
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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