• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.285-290
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• 2009

An association rule mining has been studied to find hidden data pattern in data mining. A realization of fast processing method have became a big issue because it treated a great number of transaction data. The time which is derived by association rule finding method geometrically increase according to a number of item included data. Accordingly, the process to reduce the number of rules is necessarily needed. We propose the T-algorithm that is efficient rule reduction algorithm. The T-algorithm can reduce effectively the number of association rules. Because that the T-algorithm compares transaction data item with binary format. And improves a support and a confidence between items. The performance of the proposed T-algorithm is evaluated from a simulation.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.77-87
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• 2023

Applying various association rule mining algorithms to the network intrusion detection task involves two critical issues: too large size of generated rule set which is hard to be utilized for IoT systems and hardness of control of false negative/positive rates. In this research, we propose an association rule mining algorithm based on the newly defined measures called coverage and exclusion. Coverage shows how frequently a pattern is discovered among the transactions of a class and exclusion does how frequently a pattern is not discovered in the transactions of the other classes. We compare our algorithm experimentally with the Apriori algorithm which is the most famous algorithm using the public dataset called KDDcup99. Compared to Apriori, the proposed algorithm reduces the resulting rule set size by up to 93.2 percent while keeping accuracy completely. The proposed algorithm also controls perfectly the false negative/positive rates of the generated rules by parameters. Therefore, network analysts can effectively apply the proposed association rule mining to the network intrusion detection task by solving two issues.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.104-109
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• 2006

We can offer suitable information to users analyzing the pattern of users. An association rule is one of data mining techniques which can discover the pattern. We use an association rule which considers the web page visiting time and we should the pattern analyse of users. The offered method puts the weights in Web page visiting time of the user and produces an association rule. Weight is web page visiting time unit divide to total of web page visiting time. We offer rather meaningful result the association rule by Apriori algorithm. This method that proposes in the paper offers rather meaningful result Apriori algorithm

• The KIPS Transactions:PartD
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• v.17D no.4
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• pp.311-316
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• 2010

This paper presents a weight adjusted association rule mining algorithm (WARM). Assigning weights to each strategic factor and normalizing raw scores within each strategic factor are the key ideas of the presented algorithm. It is an extension of the earlier algorithm TSAA (transitive support association Apriori) and strategic importance is reflected by considering factors such as profit, marketing value, and customer satisfaction of each item. Performance analysis based on a real world database has been made and comparison of the mining outcomes obtained from three association rule mining algorithms (Apriori, TSAA, and WARM) is provided. The result indicates that each algorithm gives distinct and characteristic behavior in association rule mining.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.443-446
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• 2007

A new association rule mining algorithm, which reflects the strategic importance of associative relationships between items, was developed and presented in this paper. This algorithm exploits the basic framework of Apriori procedures and TSAA(transitive support association Apriori) procedure developed by Hyun and Choi in evaluating non-frequent itemsets. The algorithm considers the strategic importance(weight) of feature variables in the association rule mining process. Sample feature variables of strategic importance include: profitability, marketing value, customer satisfaction, and frequency. A database with 730 transaction data set of a large scale discount store was used to compare and verify the performance of the presented algorithm against the existing Apriori and TSAA algorithms. The result clearly indicated that the new algorithm produced substantially different association itemsets according to the weights assigned to the strategic feature variables.

• The Journal of Information Technology
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• v.9 no.3
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• pp.27-32
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• 2006

If is interested and create rule between it in item that association rules buys, by negative association rules is interested to item that do not buy, it is attempt to do data Maining more effectively. It is difficult that existent methods to find negative association rules find one part of rule, or negative association rules because use more complicated algorithm than algorithm that find association rules. Therefore, this paper presents method to create negative association rules by simpler process using Boolean Analyzer that use dependency between items. And as Boolean Analyzer through an experiment, show that can find negative association rules and more various rule through comparison with other algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.2
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• pp.104-111
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• 2008

A process mining is considered to support the discovery of business process for unstructured process model, and a process mining algorithm by using the associated rule and sequence pattern of data mining is developed to extract information about processes from event-log, and to discover process of alternative, concurrent and hidden activities. Some numerical examples are presented to show the effectiveness and efficiency of the algorithm.

• Asia pacific journal of information systems
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• v.14 no.4
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• pp.87-97
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• 2004

One of the most fundamental problems in business is ranking items with respect to profit based on historical transactions. The difficulty is that the profit of one item comes from its influence on the sales of other items as well as its own sales, and that there is no well-developed algorithm for estimating overall profit of selected items. In this paper, we developed a product network based on association rule and an algorithm for profit estimation and item selection using the estimated profit ranking(EPR). As a result of computer simulation, the suggested algorithm outperforms the individual approach and the hub-authority profit ranking algorithm.