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.

An Efficient Parallel Algorithm for Mining Frequent Pattern

2012 ◽  
Vol 562-564 ◽  
pp. 876-881
Author(s):  
Guan Xun Cui ◽  
Qian Wu ◽  
Bo He ◽  
Wei Ni
Keyword(s):  
Parallel Algorithm ◽  
Association Rules ◽  
Association Rule ◽  
High Efficiency ◽  
Hash Table ◽  
Frequent Pattern ◽  
Rule Generation ◽  
Large Database ◽  
Generation Efficiency ◽  
Transaction Database

Extraction of frequent patterns in transaction-oriented database is crucial to several data mining tasks such as association rule generation, time series analysis, classification, etc. An Efficient Parallel algorithm for Mining frequent pattern (EPM) was proposed and Fast Distributed association rules Mining (FDM) algorithm was improved. Hash table technology was used to improve the generation efficiency of the 2nd candidate items . It also reduces the number of transactions in transaction database using Tid table technology. A master-slave model of parallel algorithm for mining association rules is designed in the algorithm to reduce the communication cost. The experimental results show that this algorithm has a high efficiency to deal with large database.

scholarly journals Mining Temporal Association Rules with Temporal Soft Sets

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Xiaoyan Liu ◽  
Feng Feng ◽  
Qian Wang ◽  
Ronald R. Yager ◽  
Hamido Fujita ◽  
...  
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Temporal Association ◽  
Data Sets ◽  
Rule Mining ◽  
Soft Sets ◽  
Data Set ◽  
Temporal Association Rule ◽  
Temporal Aspect ◽  
Necessary And Sufficient

Traditional association rule extraction may run into some difficulties due to ignoring the temporal aspect of the collected data. Particularly, it happens in many cases that some item sets are frequent during specific time periods, although they are not frequent in the whole data set. In this study, we make an effort to enhance conventional rule mining by introducing temporal soft sets. We define temporal granulation mappings to induce granular structures for temporal transaction data. Using this notion, we define temporal soft sets and their Q -clip soft sets to establish a novel framework for mining temporal association rules. A number of useful characterizations and results are obtained, including a necessary and sufficient condition for fast identification of strong temporal association rules. By combining temporal soft sets with NegNodeset-based frequent item set mining techniques, we develop the negFIN-based soft temporal association rule mining (negFIN-STARM) method to extract strong temporal association rules. Numerical experiments are conducted on commonly used data sets to show the feasibility of our approach. Moreover, comparative analysis demonstrates that the newly proposed method achieves higher execution efficiency than three well-known approaches in the literature.

Loss Profit Estimation Using Temporal Association Rule Mining

2016 ◽  
Vol 3 (1) ◽  
pp. 45-57 ◽  
Author(s):  
Reshu Agarwal ◽  
Mandeep Mittal ◽  
Sarla Pareek
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Timing Constraints ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Temporal Association Rule ◽  
Frequent Items

Temporal association rule mining is a data mining technique in which relationships between items which satisfy certain timing constraints can be discovered. This paper presents the concept of temporal association rules in order to solve the problem of classification of inventories by including time expressions into association rules. Firstly, loss profit of frequent items is calculated by using temporal association rule mining algorithm. Then, the frequent items in particular time-periods are ranked according to descending order of loss profits. The manager can easily recognize most profitable items with the help of ranking found in the paper. An example is illustrated to validate the results.

Ordering Policy and Inventory Classification Using Temporal Association Rule Mining

2018 ◽  
Vol 6 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Reshu Agarwal
Keyword(s):  
Inventory Management ◽  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Order Quantity ◽  
Rule Mining ◽  
Ordering Policy ◽  
Temporal Association Rule ◽  
Optimum Order ◽  
Frequent Items

A modified framework that applies temporal association rule mining to inventory management is proposed in this article. The ordering policy of frequent items is determined and inventory is classified based on loss rule. This helps inventory managers to determine optimum order quantity of frequent items together with the most profitable item in each time-span. An example is illustrated to validate the results.

Association Rule-Based Novel Incremental Updating Algorithm

2011 ◽  
Vol 317-319 ◽  
pp. 1868-1871
Author(s):  
Jian Hong Li
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Research Topic ◽  
Massive Data ◽  
Important Research ◽  
Incremental Updating ◽  
Rule Based ◽  
Important Research Topic ◽  
Transaction Database ◽  
Novel Algorithm

This paper focuses on an important research topic in data mining (DM) which heavily replies on the association rules. In order to deal with the maintenance issues within the background of the static transaction database, there are some minor changes to minimum support and confidence coefficient. A novel algorithm based on incremental updated is proposed, which is termed as NIUA (Novel Incremental Updating Algorithm). IUA uses association rules to mining the database, aiming at finding the potential information or finding the reasons from massive data.

Sign in / Sign up

Export Citation Format

Share Document