scholarly journals Temporal Association Rule Mining and Updating and Their Application to Blast Furnace in the Steel Industry

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Yinghua Han ◽  
Deshui Yu ◽  
Chunhui Yin ◽  
Qiang Zhao
Keyword(s):  
Blast Furnace ◽  
Human Experience ◽  
Superior Performance ◽  
Temporal Association ◽  
Rule Mining ◽  
Main Method ◽  
Iron And Steel ◽  
Temporal Association Rule ◽  
Multiple Variables ◽  
The Stability

Blast furnace (BF) is the main method of modern iron-making. Ensuring the stability of the BF conditions can effectively improve the quality and output of iron and steel. However, operations of BF depend on mainly human experience, which causes two problems: (1) human experience is not objective and is difficult to inherit and learn and (2) it is difficult to acquire knowledge that contains time information among multiple variables in BF. To address these problems, a data-driven method is proposed. In this article, we propose a novel and efficient algorithm for discovering underlying knowledge in the form of temporal association rules (TARs) in BF iron-making data. First, a new TAR mining framework is proposed for mining temporal frequent patterns. Then, a novel TAR mining algorithm is proposed for mining underlying, up-to-date, and effective knowledge in the form of TARs. Finally, considering the updating of the BF database, a rule updating method is proposed that is based on the algorithm that is proposed in this article. Our extensive experiments demonstrate the satisfactory performance of the proposed algorithm in discovering TARs in comparison with the state-of-the-art algorithms. Experiments on BF iron-making data have demonstrated the superior performance and practicability of the proposed method.

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.

Blast Furnace Fault Diagnosis Based on Nonlinear Fuzzy Neural Identification

2011 ◽  
Vol 204-210 ◽  
pp. 1254-1257 ◽  
Author(s):  
Lian Pan ◽  
Xu Hui
Keyword(s):  
Blast Furnace ◽  
Large Time ◽  
Economic Losses ◽  
Blast Furnace Smelting ◽  
Furnace Process ◽  
Iron And Steel ◽  
Fuzzy Neural ◽  
The Stability ◽  
Pillar Industry ◽  
Furnace Smelting

Iron and steel industry is the pillar industry, and blast furnace smelting is an important part of it. Through analysis of parameters that affect the stability of blast furnace conditions, we can determine which fault condition to happen and promptly take appropriate measures to eliminate. Thereby we can effectively reduce economic losses. For the blast furnace process has features such as nonlinear, large time-delay and strong coupling, we take use of fuzzy neural network to have furnace fault identified, in order that it can be shown at the stage of fault premonition. To do that, we can reduce or avoid the accidence.

Temporal Association Rule Mining in Event Sequences

2011 ◽  
pp. 1098-1102
Author(s):  
Sherri K. Harms
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Time Series Data ◽  
Series Data ◽  
Temporal Association ◽  
Temporal Data ◽  
Rule Mining ◽  
Event Sequences ◽  
Use Of Data ◽  
Temporal Association Rule

The emergence of remote sensing, scientific simulation and other survey technologies has dramatically enhanced our capabilities to collect temporal data. However, the explosive growth in data makes the management, analysis, and use of data both difficult and expensive. To meet these challenges, there is an increased use of data mining techniques to index, cluster, classify and mine association rules from time series data (Roddick & Spiliopoulou, 2002; Han, 2001). A major focus of these algorithms is to characterize and predict complex, irregular, or suspicious activity (Han, 2001).

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