Temporal association rule mining: An overview considering the time variable as an integral or implied component

2020 ◽  
Vol 10 (4) ◽  
Author(s):  
Alberto Segura‐Delgado ◽  
María José Gacto ◽  
Rafael Alcalá ◽  
Jesús Alcalá‐Fdez
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Time Variable ◽  
Temporal Association ◽  
Rule Mining ◽  
Temporal Association Rule

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.

Mining Changes in Temporal Patterns in Latest Time Window for Knowledge Discovery

2019 ◽  
Vol 18 (03) ◽  
pp. 1950028
Author(s):  
Sheel Shalini ◽  
Kanhaiya Lal
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Time Window ◽  
Search Space ◽  
Temporal Association ◽  
Incremental Mining ◽  
Rule Mining ◽  
Novel Approach ◽  
Temporal Association Rule ◽  
Mining Algorithms

Temporal Association Rule mining uncovers time integrated associations in a transactional database. However, in an environment where database is regularly updated, maintenance of rules is a challenging process. Earlier algorithms suggested for maintaining frequent patterns either suffered from the problem of repeated scanning or the problem of larger storage space. Therefore, this paper proposes an algorithm “Probabilistic Incremental Temporal Association Rule Mining (PITARM)” that uncovers the changed behaviour in an updated database to maintain the rules efficiently. The proposed algorithm defines two support measures to identify itemsets expected to be frequent in the successive segment in advance. It reduces unnecessary scanning of itemsets in the entire database through three-fold verification and avoids generating redundant supersets and power sets from infrequent itemsets. Implementation of pruning technique in incremental mining is a novel approach that makes it better than earlier incremental mining algorithms and consequently reduces search space to a great extent. It scans the entire database only once, thus reducing execution time. Experimental results confirm that it is an enhancement over earlier algorithms.

Temporal Association Rule Mining

2015 ◽  
pp. 247-257
Author(s):  
Ting-Feng Tan ◽  
Qing-Guo Wang ◽  
Tian-He Phang ◽  
Xian Li ◽  
Jiangshuai Huang ◽  
...  
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Rule Mining ◽  
Temporal Association Rule

Temporal Association Rule Mining: With Application to US Stock Market

2015 ◽  
Vol 3 (5) ◽  
Author(s):  
Ting-Feng Tan ◽  
Qing-Guo Wang ◽  
Xian Li ◽  
Jiangshuai Huang ◽  
Tian-He Phang
Keyword(s):  
Stock Market ◽  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Rule Mining ◽  
Temporal Association Rule

Ordering policy using temporal association rule mining

2015 ◽  
Vol 1 (2) ◽  
pp. 157 ◽  
Author(s):  
Mandeep Mittal ◽  
Sarla Pareek ◽  
Reshu Agarwal
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Rule Mining ◽  
Ordering Policy ◽  
Temporal Association Rule
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