• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.210-214
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• 2002

Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining fer taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies fer categorical attributes and generalization hierarchies of fuzzy linguistic terms fur quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights f3r attributes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.247-250
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• 2003

Due to the increasing use of very large databases, mining useful information and implicit knowledge from databases is evolving. However, most conventional data mining algorithms identify the relationship among features using binary values (TRUE/FALSE or 0/1) and find simple If-THEN rules at a single concept level. Therefore, implicit knowledge and causal relationships among features are commonly seen in real-world database and applications. In this paper, we thus introduce the mechanism of mining fuzzy association rules and constructing causal knowledge base form database. Acausal knowledge base construction algorithm based on Fuzzy Cognitive Map(FCM) and Srikant and Agrawal's association rule extraction method were proposed for extracting implicit causal knowledge from database. Fuzzy association rules are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. It integrates fuzzy set concept and causal knowledge-based data mining technologies to achieve this purpose. The proposed mechanism consists of three phases: First, adaptation of the fuzzy membership function to the database. Second, extraction of the fuzzy association rules using fuzzy input values. Third, building the causal knowledge base. A credit example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstration the effectiveness of the proposed algorithm.

• 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.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.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_1
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• pp.249-256
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• 2024

Traditional methods for monitoring targets rely heavily on probabilistic data association (PDA) or Kalman filtering. However, achieving optimal performance in a densely congested tracking environment proves challenging due to factors such as the complexities of measurement, mathematical simplification, and combined target detection for the tracking association problem. This article analyzes a target tracking problem through the lens of fuzzy logic theory, identifies the fuzzy rules that a fuzzy tracker employs, and designs the tracker utilizing fuzzy rules and Kalman filtering.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.347-353
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• 2019

Recent recommendation systems are developing toward the utilization of high-dimensional data. However, high-dimensional data can increase algorithm complexity by expanding dimensions and be lower the accuracy of recommended items. In addition, it can cause the problem of data sparsity and make it difficult to provide users with proper recommended items. This study proposed an algorithm that classify users' subjective data with objective criteria with fuzzy-AHP and make use of rules with repetitive patterns through fuzzy association rules. Trying to check how problems with high-dimensional data would be mitigated by the algorithm, we performed 5-fold cross validation according to the changing number of users. The results show that the algorithm-applied system recorded accuracy that was 12.5% higher than that of the fuzzy-AHP-applied system and mitigated the problem of data sparsity.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.206-209
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• 1999

Conventional target tracking techniques are primarily based on Kalman filtering or probabilistic data association(PDA). But it is difficult to perform well under a high cluttered tracking environment because of the difficulty of measurement, the problem of mathematical simplification and the difficulty of combined target detection for tracking association problem. This paper deals with an analysis of target tracking problem using fuzzy-logic theory, and determines fuzzy rules used by a fuzzy tracker, and designs the fuzzy tracker by using fuzzy rules and Kalman filtering.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.204-209
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• 2002

The interest of data mining in artificial intelligence with fuzzy logic has been increased. Data mining is a process of extracting desirable knowledge and interesting pattern ken large data set. Because of expansion of WWW, web data is more and more huge. Besides mining web contents and web structures, another important task for web mining is web usage mining which mines web log data to discover user access pattern. The goal of web usage mining in this paper is to find interesting user pattern in the web with user feedback. It is very important to find user's characteristic fer e-business environment. In Customer Relationship Management, recommending product and sending e-mail to user by extracted users characteristics are needed. Using our method, we extract user profile from the result of web usage mining. In this research, we concentrate on finding association rules and verify validity of them. The proposed procedure can integrate fuzzy set concept and association rule. Fuzzy association rule uses given server log file and performs several preprocessing tasks. Extracted transaction files are used to find rules by fuzzy web usage mining. To verify the validity of user's feedback, the web log data from our laboratory web server.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.240-259
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• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.