• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 춘계학술발표대회
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• pp.230-233
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• 2011

Chronic disease is linked to patient's' lifestyle. Therefore, doctor has to monitor his/her patient over time. This may involve reviewing many reports, finding any changes, and modifying several treatments. One solution to optimize the burden is using a visualizing tool over time such as a timeline-based visualization tool where all reports and medicine are integrated in a problem centric and time-based style to enable the doctor to predict and adjust the treatment plan. This solution was proposed by Bui et. al. [2] to observe the medical history of a patient. However, there was limitation of studying the diabetes patient's history to find out what was the cause of the current development in patient's condition; moreover what would be the prediction of current implication in one of the diabetes' related factors (such as fat, cholesterol, or potassium). In this paper, we propose a Grid-based Interactive Diabetes System (GIDS) to support bioinformatics analysis application for diabetes diseases. GIDS used an agglomerative clustering algorithm as clustering correlation algorithm as primary algorithm to focus medical researcher in the findings to predict the implication of the undertaken diabetes patient. The algorithm was Chronological Clustering proposed by P. Legendre [11] [12].

• 한국산학기술학회논문지
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• 제19권11호
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• pp.758-764
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• 2018

특허(patent), 학술 논문(scholarly paper)과 연구 보고서(research report)와 같은 디지털 문서(digital document)에는 주제(topic)를 요약하는 저자 키워드(author keyword)가 있다. 서로 다른 문서가 동일한 키워드를 공유하고 있다면 두 문서가 동일한 주제의 내용을 기술하고 있을 가능성이 매우 높다. 문서 군집화(document clustering)는 비슷한 주제를 가지는 문서들을 비지도 학습 방법(unsupervised learning)을 이용하여 같은 군집으로 그룹(group)화 하는 것이다. 문서 군집화는 다양한 분석에 이용되지만 대용량의 문서 데이터에 적용하기 위해서는 많은 계산량이 필요함으로 쉽지 않다. 이러한 경우, 문서의 내용을 이용하는 것보다 문서의 키워드를 이용하여 군집화하면 더욱 효율적으로 대용량의 데이터를 연결할 수 있다. 기존의 상향식 군집화 방법(bottom-up hierarchical clustering)은 대용량의 키워드 군집화(keyword clustering)를 수행하는데 있어서 많은 시간이 필요하다는 문제점이 있다. 본 논문에서는 정보검색(information retrieval)에서 널리 사용되는 역인덱스(inverted-index) 구조를 상향식 군집화에 적용한 효율적인 군집화 방법을 제안하고, 제안 방법을 대용량의 키워드 데이터에 적용하였으며, 그 결과를 분석하였다.

• 응용통계연구
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• 제24권4호
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• pp.623-631
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• 2011

시간경로 마이크로어레이 자료 분석의 주요 목적 중의 하나는 유전자들의 시간에 따른 발현수준의 변화를 고려함으로써 발현패턴에 기초한 유전자들의 그룹을 찾기 위한 것으로, 군집분석을 위한 다양한 알고리즘들이 제안되었다. 본 연구에서 시간경로 마이크로어레이 자료에 대한 군집분석을 위해 두 약물제제 간 생물학적 동등성을 평가하기 위한 약동학 시험에서 사용되는 약동학적 파라미터 값에 기초한 군집분석을 제안하였으며 이를 실제 데이터 및 모의실험 자료에 적용하여 유용성을 검토하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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• pp.3121-3143
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• 2019

Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2004년도 춘계학술발표대회
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• pp.673-676
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• 2004

Fast and high-quality document clustering algorithms play an important role in providing data exploration by organizing large amounts of information into a small number of meaningful clusters. In particular, hierarchical clustering provide a view of the data at different levels, making the large document collections are adapted to people's instinctive and interested requires. Many papers have shown that the hierarchical clustering method takes good-performance, but is limited because of its quadratic time complexity. In contrast, K-means has a time complexity that is linear in the number of documents, but is thought to produce inferior clusters. Think of the factor of simpleness, high-quality and high-efficiency, we combine the two approaches providing a new system named CONDOR system [10] with hierarchical structure based on document clustering using K-means algorithm to "get the best of both worlds". The performance of CONDOR system is compared with the VIVISIMO hierarchical clustering system [9], and performance is analyzed on feature words selection of specific topics and the optimum hierarchy depth.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.177-182
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• 2022

Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.

• 한국지능시스템학회논문지
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• 제15권6호
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• pp.688-694
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• 2005

본 논문에서는 클러스터링을 뉴로-퍼지 모델에 직접 적용하여 모델을 최적화하는 방법을 제안하였다. 기존의 오차미분기반 학습을 통한 뉴로-퍼지 모델의 최적화 과정과는 달리 제안된 방법은 클러스터링 학습과 연계하여 모델을 구성하며 자율적으로 클러스터의 수를 추정하며 동시에 최적화를 수행한다. 순차적인 학습 기법에서는 각각의 학습 기법을 따로 적용하여 모델링을 실시하였으나 제안된 기법에서는 하나의 클러스터링 학습으로 전체 모델의 학습을 실시하였다. 또한 제안된 방법에서는 클러스터링이 수렴하는 만큼 전체 모델의 연산량이 감소하여 학습과정에서 발생하는 연산량 문제를 개선하였다. 시뮬레이션을 통하여 기존의 연구 결과들과 비교하여 제안된 기법의 유용성을 보였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권4호
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• pp.256-267
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• 2014

An automatic switch among ensembles of clustering algorithms is proposed as a part of the bibliographic big data retrieval system by utilizing a fuzzy inference engine as a decision support tool to select the fastest performing clustering algorithm between fuzzy C-means (FCM) clustering, Newman-Girvan clustering, and the combination of both. It aims to realize the best clustering performance with the reduction of computational complexity from O($n^3$) to O(n). The automatic switch is developed by using fuzzy logic controller written in Java and accepts 3 inputs from each clustering result, i.e., number of clusters, number of vertices, and time taken to complete the clustering process. The experimental results on PC (Intel Core i5-3210M at 2.50 GHz) demonstrates that the combination of both clustering algorithms is selected as the best performing algorithm in 20 out of 27 cases with the highest percentage of 83.99%, completed in 161 seconds. The self-adapted FCM is selected as the best performing algorithm in 4 cases and the Newman-Girvan is selected in 3 cases.The automatic switch is to be incorporated into the bibliographic big data retrieval system that focuses on visualization of fuzzy relationship using hybrid approach combining FCM and Newman-Girvan algorithm, and is planning to be released to the public through the Internet.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2005년도 추계학술대회 및 정기총회
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• pp.51-54
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• 2005

Clustering analysis is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. In this paper we propose a new algorithm for K-medoids clustering which runs like the K-means algorithm. The new algorithm calculates distance matrix once and uses it for finding new medoids at every iterative step. We evaluate the proposed method using real and synthetic data and compare with the results of other algorithms. The proposed algorithm takes reduced time in computation and better performance than others.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 한국컴퓨터종합학술대회 논문집 Vol.32 No.1 (A)
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• pp.415-417
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• 2005

Core operations (e.9. TDMA scheduler, synchronized sleep period, data aggregation) of many proposed protocols for different layer of sensor network necessitate clock synchronization. Our Paper mingles the scheme of dynamic clustering and diffusion based asynchronous averaging algorithm for clock synchronization in sensor network. Our proposed algorithm takes the advantage of dynamic clustering and then applies asynchronous averaging algorithm for synchronization to reduce number of rounds and operations required for converging time which in turn save energy significantly than energy required in diffusion based asynchronous averaging algorithm.