• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 한국지능시스템학회 2007년도 추계학술대회 학술발표 논문집
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• pp.67-70
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• 2007

본 논문은 클러스터링 문제에서 사전 정보에 대한 활용의 효율을 개선시킬 수 있는 방법을 제안한다. 클러스터링에서 사전 정보의 존재 시 이의 활용은 성능을 개선시킬 수 있는 계기가 될 수 있으므로 그의 활용 폭을 늘리기 위한 방법으로 다양한 사용 방법의 적용인 semi-supervised 클러스터링 앙상블을 제안한다. 사전 정보의 활용 방법의 방안으로써 association-rule의 개념을 접목하였다. 클러스터 수를 다르게 적용하더라도 패턴간의 유사도가 높으면 같은 그룹에 속할 확률은 높아진다. 다양한 초기화에 따른 클러스터의 동작은 사전 정보의 활용을 다양화 시키게 되며, 사전 정보에 충족하는 각각의 클러스터 결과를 제시한다. 결과를 총 취합하여 association-matrix를 형성하면 패턴간의 유사도를 얻을 수 있으며 결국 association-matrix를 통해 클러스터링 할 수 있는 방법을 제시한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.101-105
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• 2005

본 논문에서는 LVQ(Learning Vector Quantization)을 퍼지화한 새로운 퍼지 학습 법칙을 제안하였다. 퍼지 LVQ 학습 법칙 3은 기존의 학습률 대신에 퍼지 학습률을 사용하였는데, 기존의 LVQ와는 달리 비대칭인 학습률을 사용하였다. 기본의 LVQ에서는 분류가 맞거나 틀렸을 때 같은 학습률을 사용하고 부호만 달랐으나, 새로운 퍼지 학습 법칙에서는 분류가 맞거나 틀렸을 때 부호가 다를 뿐만 아니라 학습률도 다르다. 이 새로운 퍼지 학습 법칙을 무감독 신경회로망인 improved IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망에 적용하여 감독 신경회로망으로 변형하였다. Improved IAFC 신경회로망은 유연성이 있으면서도 안정성이 있다. 제안한 supervised IAFC 신경회로망 3의 성능과 오류 역전파 신경회로망의 성능을 비교하기 위하여 iris 데이터를 사용하였는데 Supervised IAFC 신경회로망 3가 오류 역전파 신경회로망보다 성능이 우수하였다.

• Journal of Institute of Control, Robotics and Systems
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• 제4권3호
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• pp.334-341
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• 1998

This paper proposes an algorithm to obtain the time-to-contact between an observer and a target and surface orientation of the target. These two physical elements are computed from the image deformation of a known shape, which is extracted by supervised classification of detected line segments based on MAP and Mahalanobis distance. The proposed algorithm was applied to the natural outdoor traffic scene and would contribute to the development for a collision avoidance system.

• Journal of the Korean Institute of Intelligent Systems
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• 제13권2호
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• pp.237-242
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• 2003

In this paper, we propose a new adaptive intrusion detection algorithm based on clustering: Kernel-ART, which is composed of the on-line clustering algorithm, ART (adaptive resonance theory), combining with mercer-kernel and concept vector. Kernel-ART is not only satisfying all desirable characteristics in the context of clustering-based IDS but also alleviating drawbacks associated with the supervised learning IDS. It is able to detect various types of intrusions in real-time by means of generating clusters incrementally.

• Proceedings of the Korean Information Science Society Conference
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• 한국정보과학회 2008년도 한국컴퓨터종합학술대회논문집 Vol.35 No.1 (C)
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• pp.26-30
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• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed our approach consists of three stages: (i) data pre-processing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

• Journal of the Korean Institute of Intelligent Systems
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• 제12권5호
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• pp.451-461
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• 2002

Differential evolution(DE) has been preyed to be an efficient method for optimizing real-valued multi-modal objective functions. DE's main assets are its conceptual simplicity and ease of use. However, the convergence properties are deeply dependent on the control parameters of DE. This paper proposes an adaptive differential evolution(ADE) method which combines with a variant of DE and an adaptive mechanism of the control parameters. ADE contributes to the robustness and the easy use of the DE without deteriorating the convergence. 12 optimization problems is considered to test ADE. As an application of ADE the paper presents a supervised clustering method for predicting events, what is called, an evolutionary event clustering(EEC). EEC is tested for 4 cases used widely for the validation of data modeling.

• Journal of the Korea Society of Computer and Information
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• 제19권7호
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• pp.1-8
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• 2014

Clustering is one of the most popular unsupervised learning methods, which is widely used to form clusters with homogeneous data. Clustering was used to extract contexts corresponding to clusters and a classification method was applied to each context or cluster individually. However, it is difficult to say that the unsupervised clustering is the best context forming method from the view of classification. In this paper, a new clustering method considering classification was proposed. The proposed method tries to minimize classification error in each cluster when a classification method is applied to each context locally. For this purpose, the proposed method adds constraints forcing two data points belong to the same class to have small distances, and two data points belong to different classes to have large distances in each cluster like in linear discriminant analysis. The usefulness of the proposed method is confirmed by experimental results.

• Journal of KIISE
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• 제45권1호
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• pp.15-21
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• 2018

Low-Rank Representation (LRR) based methods are widely used in many practical applications, such as face clustering and object detection, because they can guarantee high prediction accuracy when used to constructing graphs in graph - based semi-supervised learning. However, in order to solve the LRR problem, it is necessary to perform singular value decomposition on the square matrix of the number of data points for each iteration of the algorithm; hence the calculation is inefficient. To solve this problem, we propose an improved and faster LRR method based on the recently published Fast LRR (FaLRR) and suggests ways to introduce and optimize additional constraints on the underlying optimization goals in order to address the fact that the FaLRR is fast but actually poor in classification problems. Our experiments confirm that the proposed method finds a better solution than LRR does. We also propose Fast MLRR (FaMLRR), which shows better results when the goal of minimizing is added.

• International Journal of Control, Automation, and Systems
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• 제2권1호
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• pp.118-124
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• 2004

This paper proposes an approach to deriving a fuzzy classifier based on evolutionary supervised clustering, which identifies the optimal clusters necessary to classify classes. The clusters are formed by multi-dimensional weighted Euclidean distance, which allows clusters of varying shapes and sizes. A cluster induces a Gaussian fuzzy antecedent set with unique variance in each dimension, which reflects the tightness of the cluster. The fuzzy classifier is com-posed of as many classification rules as classes. The clusters identified for each class constitute fuzzy sets, which are joined by an "and" connective in the antecedent part of the corresponding rule. The approach is evaluated using six data sets. The comparative results with different classifiers are given.are given.

• Smart Structures and Systems
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• 제21권2호
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• pp.225-234
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• 2018

E-Health allows you to supersede the central patient wireless healthcare system. Wireless Body Sensor Network (WBSN) is the first phase of the e-Health system. In this paper, we aim to understand e-Health architecture and configuration, and attempt to minimize energy consumption and latency in transmission routing protocols during restrictive latency in data delivery of WBSN phase. The goal is to concentrate on polling protocol to improve and optimize the routing time interval and schedule communication to reduce energy utilization. In this research, two types of network models routing protocols are proposed - elemental and clustering. The elemental model improves efficiency by using a polling protocol, and the clustering model is the extension of the elemental model that Destruct Supervised Decision Tree (DSDT) algorithm has been proposed to solve the time interval conflict transmission. The simulation study verifies that the proposed models deliver better performance than the existing BSN protocol for WBSN.