• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.241-251
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• 2006

Data mining is the method to find useful information for large amounts of data in database It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze industrial waste database using data mining technique. We use k-means algorithm for clustering and C5.0 algorithm for decision tree and Apriori algorithm for association rule. We can use these analysis outputs for environmental preservation and environmental improvement.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.11
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• pp.2711-2722
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• 1993

Most of the current 2-D object recognition systems are model-based. In such systems, the representation of each of a known set of objects are precompiled and stored in a database of models. Later, they are used to recognize the image of an object in each instance. In this thesis, the approach method for the 2-D object recognition is treating an object boundary as a string of structral units and utilizing string matching to analyze the scenes. To reduce string matching time, models are rebuilt by means of fuzzy c-means clustering algorithm. In this experiments, the image of objects were taken at initial position of a robot from the CCD camera, and the models are consturcted by the proposed algorithm. After that the image of an unknown object is taken by the camera at a random position, and then the unknown object is identified by a comparison between the unknown object and models. Finally, the amount of translation and rotation of object from the initial position is computed.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.485-492
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• 2009

In the mobile service environment, patterns generated by temporal synchronization are streamed with different instance values. This paper proposed a variable clustering management method, which manages multiple data streaming dynamically, to support flexible clustering. The method manages synchronization effectively and differently with conventional streaming methods in data streaming environment and manages clustering streaming after the structural presentation level and the fitness presentation level. In the structural presentation level, the stream structure is presented using level matching and accumulation matching, and clustering management is carried out by the management of dynamic segment and static segment. The performance of the proposed method is tested by using k-means method, C/S server method, CDN method, and simulation. The test results showed that the proposed method has better performance than the other methods.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.842-848
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• 2008

This paper deal with uncertainty problem by using Type-2 fuzzy logic set for nonlinear system modeling. We design Type-2 fuzzy logic system in which the antecedent and the consequent part of rules are given as Type-2 fuzzy set and also analyze the performance of the ensuing nonlinear model with uncertainty. Here, the apexes of the antecedent membership functions of rules are decided by C-means clustering algorithm and the apexes of the consequent membership functions of rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The proposed model is demonstrated with the aid of two representative numerical examples, such as mathematical synthetic data set and Mackey-Glass time series data set and also we discuss the approximation as well as generalization abilities for the model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2108-2116
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• 2008

The paper concerns Fuzzy C-Means clustering based Radial Basis Function neural networks (FCM-RBFNN) and the optimization of the network is carried out by means of Particle Swarm Optimization(PSO). FCM-RBFNN is the extended architecture of Radial Basis Function Neural Network(RBFNN). In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM - RBFNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Weighted Least Square Estimator(WLSE) are used to estimates the coefficients of polynomial. Since the performance of FCM-RBFNN is affected by some parameters of FCM-RBFNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the PSO is exploited to carry out the structural as well as parametric optimization of FCM-RBFNN. Moreover The proposed model is demonstrated with the use of numerical example and gas furnace data set.

• Journal of IKEEE
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• v.11 no.3
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• pp.100-107
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• 2007

One of the fundamental problems in wireless sensor networks is the efficient deployment of sensor nodes. The Fuzzy C-Means(FCM) clustering algorithm is proposed to determine the optimum location and minimum number of sensor nodes for the specific application space. We performed a simulation and a experiment using two rectangular and one L shape area. We found the minimum number of sensor nodes for the complete coverage of modeled area, and discovered the optimum location of each nodes. The real deploy experiment using sensor nodes shows the 94.6%, 92.2% and 95.7% error free communication rate respectively.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.139-142
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• 1998

Missing values in data for fuzzy c-menas clustering is discussed. Two basic methods of fuzzy c-means, i.e., the standard fuzzy c-means and the entropy method are considered and three options of handling missing values are proposed, among which one is to define a new distance between data with missing values, second is to alter a weight in the new distance, and the third is to fill the missing values by an appropriate numbers. Experimental Results are shown.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.345-350
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• 1999

The effectiveness of software error compensation for thermally induced machine tool errors relies on the prediction accuracy of the pre-established thermal error models. The selection of optimal sensor locations is the most important in establishing these empirical models. In this paper, a methodology for the selection of optimal sensor locations is proposed to establish a robust linear model which is not subjected to collinearity. Correlation coefficient and time delay are used as thermal parameters for optimal sensor location. Firstly, thermal deformation and temperatures are measured with machine tools being excited by sinusoidal heat input. And then, after correlation coefficient and time delays are calculated from the measured data, the optimal sensor location is selected through hard c-means clustering and sequential selection method. The validity of the proposed methodology is verified through the estimation of thermal expansion along Z-axis by spindle rotation.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.336-343
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• 2017

We propose a real time personalized recommendation algorithm on the mobile device. We use a unified collaborative filtering with reduced data. We use Fuzzy C-means clustering to obtain the reduced data and Konohen SOM is applied to get initial values of the cluster centers. The proposed algorithm overcomes data sparsity since it extends data to the similar users and similar items. Also, it enables real time service on the mobile device since it reduces computing time by data clustering. Applying the suggested algorithm to the MovieLens data, we show that the suggested algorithm has reasonable performance in comparison with collaborative filtering. We developed Android-based smart-phone application, which recommends restaurants with coupons and restaurant information.