• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권1호
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• pp.31-36
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• 2010

Many clustering algorithms have been used in diverse fields. When we need to group given data set into clusters, many clustering algorithms based on similarity or distance measures are considered. Most clustering works have been based on hierarchical and non-hierarchical clustering algorithms. Generally, for the clustering works, researchers have used clustering algorithms case by case from these algorithms. Also they have to determine proper clustering methods subjectively by their prior knowledge. In this paper, to solve the subjective problem of clustering we make empirical comparisons of popular clustering algorithms which are hierarchical and non hierarchical techniques using Silhouette measure. We use silhouette information to evaluate the clustering results such as the number of clusters and cluster variance. We verify our comparison study by experimental results using data sets from UCI machine learning repository. Therefore we are able to use efficient and objective clustering algorithms.

• 정보처리학회논문지B
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• 제11B권4호
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• pp.485-490
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• 2004

자조의 표현에서 군집화는 주어진 데이터를 서로 유사한 개체들끼리 몇 개의 집단으로 묶는 작업을 수행한다. 군집화의 유사도 결정 측도는 맡은 연구들에서 매우 다양한 것들이 사용되었다. 하지만 군집화 결과의 성능 측정에 대한 객관적인 기준 설정이 어렵기 때문에 군집화 결과에 대한 해석은 매우 주관적이고, 애매한 경우가 많다. 퍼지 군집화는 이러한 주관적인 군집화 문제에 있어서 객관성 있는 군집 결정 방안을 제시하여 준다. 각 개체들이 특정 군집에 속하게 될 퍼지 멤버 함수값을 원소로 하는 유사도 행렬을 통하여 군집화를 수행한다. 본 논문에서는 차원 축소기법의 하나인 주성분 분석과 강력한 통계적 학습 이론인 베이지안 학습을 결합한 군집화 모형을 제안하여, 객관적인 퍼지 군집화를 수행하였다. 제안 알고리즘의 성능 평가를 위하여 UCI Machine Loaming Repository의 Iris와 Glass Identification 데이터를 이용한 실험 결과를 제시하였다.

• Journal of Information Processing Systems
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• 제19권6호
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• pp.778-790
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• 2023

Although density peak clustering can often easily yield excellent results, there is still room for improvement when dealing with complex, high-dimensional datasets. One of the main limitations of this algorithm is its reliance on geometric distance as the sole similarity measurement. To address this limitation, we draw inspiration from the information bottleneck theory, and propose a novel density peak clustering algorithm that incorporates this theory as a similarity measure. Specifically, our algorithm utilizes the joint probability distribution between data objects and feature information, and employs the loss of mutual information as the measurement standard. This approach not only eliminates the potential for subjective error in selecting similarity method, but also enhances performance on datasets with multiple centers and high dimensionality. To evaluate the effectiveness of our algorithm, we conducted experiments using ten carefully selected datasets and compared the results with three other algorithms. The experimental results demonstrate that our information bottleneck-based density peaks clustering (IBDPC) algorithm consistently achieves high levels of accuracy, highlighting its potential as a valuable tool for data clustering tasks.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.196-201
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• 2008

Statistical learning theory has three analytical tools which are support vector machine, support vector regression, and support vector clustering for classification, regression, and clustering respectively. In general, their performances are good because they are constructed by convex optimization. But, there are some problems in the methods. One of the problems is the subjective determination of the parameters for kernel function and regularization by the arts of researchers. Also, the results of the learning machines are depended on the selected parameters. In this paper, we propose an efficient method for objective determination of the parameters of support vector clustering which is the clustering method of statistical learning theory. Using evolutionary algorithm and bootstrap method, we select the parameters of kernel function and regularization constant objectively. To verify improved performances of proposed research, we compare our method with established learning algorithms using the data sets form ucr machine learning repository and synthetic data.

• 한국멀티미디어학회논문지
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• 제24권1호
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• pp.106-116
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• 2021

To increase the quality of the system in the subjective-type question grading and document classification, we need the paragraph detection. But it is not easy because it is accompanied by semantic analysis. Many researches on the paragraph detection solve the detection problem using the word based clustering method. However, the word based method can not use the order and dependency relation between words. This paper suggests the paragraph detection system using syntactic-semantic relation between words with the Korean syntactic-semantic analysis. This system is the hybrid system of word based, concept based, and syntactic-semantic tree based detection. The experiment result of the system shows it has the better result than the word based system. This system will be utilized in Korean subjective question grading and document classification.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.330-333
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• 2003

Initial cluster size for clustering of partitioning methods is very important to the clustering result. In K-means algorithm, the result of cluster analysis becomes different with optimal cluster size K. Usually, the initial cluster size is determined by prior and subjective information. Sometimes this may not be optimal. Now, more objective method is needed to solve this problem. In our research, we propose a hybrid genetic algorithm, a tree induction based evolution algorithm, for determination of optimal cluster size. Initial population of this algorithm is determined by the number of terminal nodes of tree induction. From the initial population based on decision tree, our optimal cluster size is generated. The fitness function of ours is defined an inverse of dissimilarity measure. And the bagging approach is used for saying computational time cost.

• 전기학회논문지
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• 제62권4호
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• pp.544-553
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• 2013

In this paper, we propose the pattern classifier of Radial Basis Function Neural Networks(RBFNNs) for diagnosis of 3-phase partial discharge. Conventional methods map the partial discharge/noise data on 3-PARD map, and decide whether the partial discharge occurs or not from 3-phase or neutral point. However, it is decided based on his own subjective knowledge of skilled experter. In order to solve these problems, the mapping of data as well as the classification of phases are considered by using the general 3-PARD map and PA method, and the identification of phases occurring partial discharge/noise discharge is done. In the sequel, the type of partial discharge occurring on arbitrary random phase is classified and identified by fuzzy clustering-based polynomial Radial Basis Function Neural Networks(RBFNN) classifier. And by identifying the learning rate, momentum coefficient, and fuzzification coefficient of FCM fuzzy clustering with the aid of PSO algorithm, the RBFNN classifier is optimized. The virtual simulated data and the experimental data acquired from practical field are used for performance estimation of 3-phase partial discharge pattern classifier.

• 미생물학회지
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• 제39권2호
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• pp.67-75
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• 2003

비교유전체학의 주요 주제 중 유전자서열을 분류하고 단백질기능을 예측하는 연구가 있으며, 이를 위해 단백질 구조, 공통서열 및 바인딩 위치 예측등의 방법과 함께, 전유전체 서열에서 구해지는 유사도 그래프를 분석해 상동유전자를 검색하는 계산학적인 접근방법이 있다. 유사도그래프를 사용한 방법은 서열에 대한 기존 지식에 의존하지 않는 장점이 있지만 유사도 하한값과 같은 주관적인 임계값이 필요한 단점이 있다. 본 논문에서는 반복적으로 그래프를 분해하는 이전의 방법을 일반화시켜, 유사도 그래프에 기반한 유전자 서열군집분석 방법론과 객관적이고 안정적인 파라미터 임계값 계산 방법을 제안한다. 제시된 방법으로 알려진 미생물 유전체 서 열을 분석하여 이전의 방법인 BAG 알고리즘 결과와 비교했다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제26권9호
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• pp.1114-1124
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• 1999

클러스터링은 동일한 클러스터에 속하는 데이타들 간에는 유사도가 크도록 하고 다른 클러스터에 속하는 데이타들 간에는 유사도가 작도록 주어진 데이타를 몇 개의 클러스터로 묶는 것이다. 어떤 대상을 기술하는 데이타는 수치 속성뿐만 아니라 정성적인 비수치 속성을 갖게 되고, 이들 속성값은 관측 오류, 불확실성, 주관적인 판정 등으로 인해서 정확한 값으로 주어지지 않고 애매한 값으로 주어지는 경우가 많다. 본 논문에서는 애매한 값을 퍼지값으로 표현하는 수치 속성과 비수치 속성을 포함한 데이타에 대한 비유사도 척도를 제안하고, 이 척도를 이용하여 퍼지값을 포함한 데이타에 대하여 퍼지 클러스터링하는 방법을 소개한 다음, 이를 이용한 실험 결과를 보인다. Abstract The objective of clustering is to group a set of data into some number of clusters in a way to minimize the similarity between data belonging to different clusters and to maximize the similarity between data belonging to the same cluster. Many data for real world objects consist of numeric attributes and non-numeric attributes whose values are fuzzily described due to observation error, uncertainty, subjective judgement, and so on. This paper proposes a dissimilarity measure applicable to such data and then introduces a fuzzy clustering method for such data using the proposed dissimilarity measure. It also presents some experiment results to show the applicability of the proposed clustering method and dissimilarity measure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.