scholarly journals Effective Application of Improved Profit-Mining Algorithm for the Interday Trading Model

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yu-Lung Hsieh ◽  
Don-Lin Yang ◽  
Jungpin Wu
Keyword(s):  
Financial Markets ◽  
Real World ◽  
Traditional Approach ◽  
Rule Mining ◽  
Mining Algorithm ◽  
Large Databases ◽  
Real World Applications ◽  
Trading Model ◽  
Mining Algorithms ◽  
Infant Stage

Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets.

Ordering policy estimation for high utility item-sets considering negative item values in large databases

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Data Mining ◽  
Real World ◽  
Defective Items ◽  
Utility Mining ◽  
Mining Algorithm ◽  
Negative Item ◽  
Ordering Policy ◽  
Large Databases ◽  
Real World Applications ◽  
High Utility

Utility mining with negative item values has recently received interest in the data mining field due to its practical considerations. Previously, the values of utility item-sets have been taken into consideration as positive. However, in real-world applications an item-set may be related to negative item values. This paper presents a method for redesigning the ordering policy by including high utility item-sets with negative items. Initially, utility mining algorithm is used to find high utility item-sets. Then, ordering policy is estimated for high utility items considering defective and non-defective items. A numerical example is illustrated to validate the results

PROVIDING TIMELY UPDATED SEQUENTIAL PATTERNS IN DECISION MAKING

2010 ◽  
Vol 09 (06) ◽  
pp. 873-888 ◽  
Author(s):  
TZUNG-PEI HONG ◽  
CHING-YAO WANG ◽  
CHUN-WEI LIN
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Real World ◽  
Experimental Results ◽  
Sequential Patterns ◽  
The Past ◽  
Large Databases ◽  
Real World Applications ◽  
Critical Task ◽  
Maintenance Process

Mining knowledge from large databases has become a critical task for organizations. Managers commonly use the obtained sequential patterns to make decisions. In the past, databases were usually assumed to be static. In real-world applications, however, transactions may be updated. In this paper, a maintenance algorithm for rapidly updating sequential patterns for real-time decision making is proposed. The proposed algorithm utilizes previously discovered large sequences in the maintenance process, thus greatly reducing the number of database rescans and improving performance. Experimental results verify the performance of the proposed approach. The proposed algorithm provides real-time knowledge that can be used for decision making.

scholarly journals EARMGA and Apriori Algorithm's Performance Evaluation for Association Rule Mining

2019 ◽  
Vol 9 (1) ◽  
pp. 6982-6987
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
The Other ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Large Databases ◽  
Algorithm Comparison ◽  
Average Confidence ◽  
Mining Algorithms

Association rule mining techniques are important part of data mining to derive relationship between attributes of large databases. Association related rule mining have evolved huge interest among researchers as many challenging problems can be solved using them. Numerous algorithms have been discovered for deriving association rules effectively. It has been evaluated that not all algorithms can give similar results in all scenarios, so decoding these merits becomes important. In this paper two association rule mining algorithms were analyzed, one is popular Apriori algorithm and the other is EARMGA (Evolutionary Association Rules Mining with Genetic Algorithm). Comparison of these two algorithms were experimentally performed based on different datasets and different parameters like Number of rules generated, Average support, Average Confidence, Covered records were detailed.

A FUZZY DATA MINING ALGORITHM FOR INCREMENTAL MINING OF QUANTITATIVE SEQUENTIAL PATTERNS

2005 ◽  
Vol 13 (06) ◽  
pp. 633-652 ◽  
Author(s):  
R. B. V. SUBRAMANYAM ◽  
A. GOSWAMI
Keyword(s):  
Data Mining ◽  
Real World ◽  
Uncertain Data ◽  
Sequential Patterns ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Incremental Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithm ◽  
Real World Applications

In real world applications, the databases are constantly added with a large number of transactions and hence maintaining latest sequential patterns valid on the updated database is crucial. Existing data mining algorithms can incrementally mine the sequential patterns from databases with binary values. Temporal transactions with quantitative values are commonly seen in real world applications. In addition, several methods have been proposed for representing uncertain data in a database. In this paper, a fuzzy data mining algorithm for incremental mining of sequential patterns from quantitative databases is proposed. Proposed algorithm called IQSP algorithm uses the fuzzy grid notion to generate fuzzy sequential patterns validated on the updated database containing the transactions in the original database and in the incremental database. It uses the information about sequential patterns that are already mined from original database and avoids start-from-scratch process. Also, it minimizes the number of candidates to check as well as number of scans to original database by identifying the potential sequences in incremental database.

Maintenance of Association Rules Using Pre-Large Itemsets

2011 ◽  
pp. 44-60 ◽  
Author(s):  
Tzung-Pei Hong ◽  
Ching-Yao Wang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Real World ◽  
Incremental Mining ◽  
Maintenance Costs ◽  
The Past ◽  
Mining Algorithm ◽  
Real World Applications ◽  
Novel Algorithm

Developing an efficient mining algorithm that can incrementally maintain discovered information as a database grows is quite important in the field of data mining. In the past, we proposed an incremental mining algorithm for maintenance of association rules as new transactions were inserted. Deletion of records in databases is, however, commonly seen in real-world applications. In this chapter, we first review the maintenance of association rules from data insertion and then attempt to extend it to solve the data deletion issue. The concept of pre-large itemsets is used to reduce the need for rescanning the original database and to save maintenance costs. A novel algorithm is proposed to maintain discovered association rules for deletion of records. The proposed algorithm doesn’t need to rescan the original database until a number of records have been deleted. If the database is large, then the number of deleted records allowed will be large too. Therefore, as the database grows, our proposed approach becomes increasingly efficient. This characteristic is especially useful for real-world applications.

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