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.

DISTRIBUTED MINING OF ASSOCIATION RULES BASED ON REDUCING THE SUPPORT THRESHOLD

2008 ◽  
Vol 17 (06) ◽  
pp. 1109-1129 ◽  
Author(s):  
BASILIS BOUTSINAS ◽  
COSTAS SIOTOS ◽  
ANTONIS GEROLIMATOS
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Rule Mining ◽  
Data Set ◽  
Processing Power ◽  
Support Threshold ◽  
Empirical Tests

One of the most important data mining problems is learning association rules of the form "90% of the customers that purchase product x also purchase product y". Discovering association rules from huge volumes of data requires substantial processing power. In this paper we present an efficient distributed algorithm for mining association rules that reduces the time complexity in a magnitude that renders as suitable for scaling up to very large data sets. The proposed algorithm is based on partitioning the initial data set into subsets and processing each subset in parallel. The proposed algorithm can maintain the set of association rules that are extracted when applying an association rule mining algorithm to all the data, by reducing the support threshold during processing the subsets. The above are confirmed by empirical tests that we present and which also demonstrate the utility of the method.

Modified Ranking With Temporal Association Rule Mining in Supply Chains

2020 ◽  
Vol 11 (4) ◽  
pp. 58-71
Author(s):  
Reshu Agarwal
Keyword(s):  
Inventory Management ◽  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Rule Mining ◽  
Individual Item ◽  
Data Set ◽  
Abc Classification ◽  
Temporal Association Rule ◽  
Time Periods

This article deals with data mining applications for the supply chain inventory management. ABC classification is usually used for inventory items classification because the number of inventory items is so large that it is not computationally feasible to set stock and service control guidelines for each individual item. Moreover, in ABC classification, the inter-relationship between items is not considered. But practically, the sale of one item could affect the sale of other items (cross selling effect). Hence, within time-periods, the inventories should be classified. In this article, a modified approach is proposed considering both time-periods and cross-selling effect to rank inventory items. A numerical example and an empirical study with a data set are used to evaluate the proposed approach. It is illustrated that by using this modified approach, the ranking of items may get affected resulting in higher profit.

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.

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.

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.

scholarly journals AN OPTIMIZED ARM SCHEME FOR DISTINCT NETWORK DATA SET

2015 ◽  
pp. 191-195
Author(s):  
K.GANESH KUMAR ◽  
H.VIGNESH RAMAMOORTHY ◽  
M.PREM KUMAR ◽  
S. SUDHA
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Distributed Databases ◽  
Research Area ◽  
Sequential Algorithm ◽  
Data Sets ◽  
Rule Mining ◽  
Data Set ◽  
Communication Costs

Association rule mining (ARM) discovers correlations between different item sets in a transaction database. It provides important knowledge in business for decision makers. Association rule mining is an active data mining research area and most ARM algorithms cater to a centralized environment. Centralized data mining to discover useful patterns in distributed databases isn't always feasible because merging data sets from different sites incurs huge network communication costs. In this paper, an improved algorithm based on good performance level for data mining is being proposed. In local sites, it runs the application based on the improved LMatrix algorithm, which is used to calculate local support counts. Local Site also finds a center site to manage every message exchanged to obtain all globally frequent item sets. It also reduces the time of scan of partition database by using LMatrix which increases the performance of the algorithm. Therefore, the research is to develop a distributed algorithm for geographically distributed data sets that reduces communication costs, superior running efficiency, and stronger scalability than direct application of a sequential algorithm in distributed databases.

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