• 음성과학
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• 제10권4호
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• pp.159-166
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• 2003

To reduce the high dimensionality required for training of feature vectors in speaker identification, we propose an efficient GMM based on local PCA with Fuzzy clustering. The proposed method firstly partitions the data space into several disjoint clusters by fuzzy clustering, and then performs PCA using the fuzzy covariance matrix in each cluster. Finally, the GMM for speaker is obtained from the transformed feature vectors with reduced dimension in each cluster. Compared to the conventional GMM with diagonal covariance matrix, the proposed method needs less storage and shows faster result, under the same performance.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.671-672
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• 2008

K-means is a popular one in clustering algorithms, and it minimizes the mutual euclidean distance among the sample points. But K-means has some demerits, such as depending on initial condition, unsupervised learning and local optimum. However mahalanobis distancecan deal this case well. In this paper, the author proposed a new clustering algorithm, named exponential probability clustering, which applied Mahalanobis distance into K-means clustering. This new clustering does possess not only the probability interpretation, but also clustering merits. Finally, the simulation results also demonstrate its good performance compared to K-means algorithm.

• 정보관리학회지
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• 제21권3호
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• pp.251-267
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• 2004

이 연구에서는 전통적인 인쇄매체 환경에서 지식에 대해 지역적인 접근법을 제공하는 권말색인과 목차의 기능에 착안하여 용어 클러스터링 실험과 클러스터 대표어 선정 실험을 통해 개별문서의 지식구조 자동 생성 기법을 제안하였다. 자동 생성된 지식구조가 갖는 기능성을 평가하여 정보 검색 환경에서의 적용 가능성을 확인하였다. 용어 클러스터링 실험에서는 워드 기법의 성능이 중복 분류를 허용하는 퍼지 K-means 클러스터링 기법에 비해 높았으며, 클러스터 대표어 선정 기법으로는 단락빈도를 이용한 경우가 가장 좋은 성능을 나타냈다. 또한, 이용자 태스크를 기반으로 하여 최종적으로 생성된 지식구조의 기능성을 평가한 결과, 이 연구에서 자동 생성된 지식구조가 인쇄매체 환경에서의 권말색인과 목차가 갖는 기능을 어느 정도 수행한다는 것을 입증하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권4호
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• pp.254-268
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• 2013

For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-type objective function. We highlight the advantages and disadvantages of each method. In addition to the completely unsupervised algorithms, we also provide an overview of some semi-supervised fuzzy kernel clustering algorithms. These algorithms use partial supervision information to guide the optimization process and avoid local minima. We also provide an overview of the different approaches that have been used to extend kernel clustering to handle very large data sets.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1995년도 학술대회
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• pp.83-86
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• 1995

Many image display devices allow only a limited number of colors to be simultaneously displayed. In displaying of natural color image using color palette, it is necessary to construct an optimal color palette and map each pixel of the original image to a color palette with fast. In this paper, we proposed the clustering algorithm using local region block centered one color cluster in the prequantized 3-D histogram. Cluster pairs which have the least distortion error are merged by considering distortion measure. The clustering process is continued until to obtain the desired number of colors. Same as the clustering process, original color image is mapped to palette color via a local region block centering around prequantized original color value. The proposed algorithm incorporated with a spatial activity weighting value which is smoothing region. The method produces high quality display images and considerably reduces computation time.

• 전기학회논문지
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• 제58권6호
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• pp.1230-1237
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• 2009

In this paper, we develop a design methodology of Granular-based Radial Basis Function Neural Networks(GRBFNN) by context-based clustering. In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The output space is granulated making use of the K-Means clustering while the input space is clustered with the aid of a so-called context-based fuzzy clustering. The number of information granules produced for each context is adjusted so that we satisfy a certain reconstructability criterion that helps us minimize an error between the original data and the ones resulting from their reconstruction involving prototypes of the clusters and the corresponding membership values. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the values of the context and the prototypes in the input space. The other parameters of these local functions are subject to further parametric optimization. Numeric examples involve some low dimensional synthetic data and selected data coming from the Machine Learning repository.

• 인터넷정보학회논문지
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• 제13권5호
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• pp.1-8
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• 2012

본 논문에서는 수치적 데이터를 이용하여 규칙을 생성하는 시스템에 대해 순차적인 클러스터링 방법을 제안한다. 단일 클러스터링 기법은 방대하고 복잡한 공간 내에서는 원하는 결과를 얻지 못할 수 있다. 이런 문제점을 해결하기 위해 제안된 방법은 서로 다른 클러스터링 기법을 순차적으로 수행하여 장점들은 활용하고 단점들은 보안하는 형태를 제안하였다. Mountain 클러스터링과 Chen 클러스터링을 이용하여 non-parametric 공간에서 자율적으로 클러스터를 구성하였고, global 공간과 local 공간으로 역할을 분담하여 클러스터를 추정한다. 추정된 클러스터들은 신경회로망이나 퍼지 시스템과 같은 지능 시스템의 구조와 초기 파라미터 결정에 활용될 수 있으며, 확장하여 헬스케어와 의료 분야에서의 결정 제공 시스템의 학습에 도움을 줄 수 있다. 제안된 방법을 유용성을 시뮬레이션을 통해 보이고자 한다.

• 산업경영시스템학회지
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• 제40권2호
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 통신소사이어티 추계학술대회논문집
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• pp.135-139
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• 2003

Sensor networks consist of sensor nodes with small-size, low-cost, low-power, and multi-functions to sense, to process and to communicate. Minimizing power consumption of sensors is an important issue in sensor networks due to limited power in sensor networks. Clustering is an efficient way to reduce data flow in sensor networks and to maintain less routing information. In this paper, we propose a multi-hop clustering mechanism using global and local ID to reduce transmission power consumption and an efficient routing method for improved data fusion and transmission.

• 전기학회논문지
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• 제65권9호
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• pp.1541-1550
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• 2016

In this paper, we introduce a novel learning methodology of fuzzy clustering-based neural network pattern classifier. Fuzzy clustering-based neural network pattern classifier depicts the patterns of given classes using fuzzy rules and categorizes the patterns on unseen data through fuzzy rules. Least squares estimator(LSE) or weighted least squares estimator(WLSE) is typically used in order to estimate the coefficients of polynomial function, but this study proposes a novel coefficient estimate method which includes advantages of the existing methods. The premise part of fuzzy rule depicts input space as "If" clause of fuzzy rule through fuzzy c-means(FCM) clustering, while the consequent part of fuzzy rule denotes output space through polynomial function such as linear, quadratic and their coefficients are estimated by the proposed local least squares estimator(LLSE)-based learning. In order to evaluate the performance of the proposed pattern classifier, the variety of machine learning data sets are exploited in experiments and through the comparative analysis of performance, it provides that the proposed LLSE-based learning method is preferable when compared with the other learning methods conventionally used in previous literature.