• Title/Summary/Keyword: K-Means Clustering

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Extension of the Possibilistic Fuzzy C-Means Clustering Algorithm (Possibilistic Fuzzy C-Means 클러스터링 알고리즘의 확장)

  • Heo, Gyeong-Yong;U, Yeong-Un;Kim, Gwang-Baek
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2007.11a
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    • pp.423-426
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    • 2007
  • 클러스터링은 주어진 데이터 포인트들을 주어진 개수의 그룹으로 나누는 비지도 학습의 한 방법이다. 클러스터링의 방법 중 하나로 널리 알려진 퍼지 클러스터링은 하나의 포인트가 모든 클러스터에 서로 다른 정도로 소속될 수 있도록 함으로써 각 포인트가 하나의 클러스터에만 속할 수 있도록 하는 K-means와 같은 방법에 비해 자연스러운 클러스터 형태의 유추가 가능하고, 잡음에 강한 장점이 있다. 이 논문에서는 기존의 퍼지 클러스터링 방법 중 소속도(membership)와 전형성(typicality)을 동시에 계산해 낼 수 있는 Possibilistic Fuzzy C-Means (PFCM) 방법에 Gath-Geva (GG)의 방법 을 적용하여 PFCM을 확장한다. 제안한 방법은 PFCM의 장점을 그대로 가지면서도, GG의 거리 척도에 의해 클러스터들 사이의 경계를 강조함으로써 분류 목적에 적합한 소속도를 계산할 수 있으며, 전형성은 가우스 형태의 분포에서 생성된 포인트들의 분포 함수를 정확하게 모사함으로써 확률 밀도 추정의 방법으로도 사용될 수 있다. 또한 GG 방법은 Gustafson-Kessel 방법과 달리 클러스터에 포함된 포인트의 개수가 확연히 차이 나는 경우에도 정확한 결과를 얻을 수 있다는 사실을 실험 결과를 통해 확인할 수 있었다.

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Energy Efficient Cluster Routing Method Using Machine Learning in WSN (무선 센서 네트워크에서의 머신러닝을 활용한 에너지 효율적인 클러스터 라우팅 방안 연구)

  • Mi-Young, Kang
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.27 no.1
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    • pp.124-130
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    • 2023
  • In this paper, we intend to improve the network lifetime by improving the energy efficiency of sensor nodes in a wireless sensor network by utilizing machine learning using K-means clustering algorithm. A wireless sensor network is a wireless network composed of physical devices including batteries as physical sensors. Due to the characteristics of sensor nodes, all resources must be efficiently used to minimize energy consumption to maximize network lifetime. A cluster based approach is used to manage groups of relatively large numbers of nodes. In the proposed protocol, by improving the existing LEACH algorithm, we propose a clustering algorithm that selects a cluster head using a cluster based approach and a location based approach. The performance results to be improved were measured using Matlab simulation. Through the experimental results, K-means clustering was applied to the energy efficiency part. By utilizing K-means, it is confirmed that energy efficiency is improved and the lifetime of the entire network is extended.

Development of Mining model through reproducibility assessment in Adverse drug event surveillance system (약물부작용감시시스템에서 재현성 평가를 통한 마이닝 모델 개발)

  • Lee, Young-Ho;Yoon, Young-Mi;Lee, Byung-Mun;Hwang, Hee-Joung;Kang, Un-Gu
    • Journal of the Korea Society of Computer and Information
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    • v.14 no.3
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    • pp.183-192
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    • 2009
  • ADESS(Adverse drug event surveillance system) is the system which distinguishes adverse drug events using adverse drug signals. This system shows superior effectiveness in adverse drug surveillance than current methods such as volunteer reporting or char review. In this study, we built clinical data mart(CDM) for the development of ADESS. This CDM could obtain data reliability by applying data quality management and the most suitable clustering number(n=4) was gained through the reproducibility assessment in unsupervised learning techniques of knowledge discovery. As the result of analysis, by applying the clustering number(N=4) K-means, Kohonen, and two-step clustering models were produced and we confirmed that the K-means algorithm makes the most closest clustering to the result of adverse drug events.

Improved TI-FCM Clustering Algorithm in Big Data (빅데이터에서 개선된 TI-FCM 클러스터링 알고리즘)

  • Lee, Kwang-Kyug
    • Journal of IKEEE
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    • v.23 no.2
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    • pp.419-424
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    • 2019
  • The FCM algorithm finds the optimal solution through iterative optimization technique. In particular, there is a difference in execution time depending on the initial center of clustering, the location of noise, the location and number of crowded densities. However, this method gradually updates the center point, and the center of the initial cluster is shifted to one side. In this paper, we propose a TI-FCM(Triangular Inequality-Fuzzy C-Means) clustering algorithm that determines the cluster center density by maximizing the distance between clusters using triangular inequality. The proposed method is an effective method to converge to real clusters compared to FCM even in large data sets. Experiments show that execution time is reduced compared to existing FCM.

Pruning Methodology for Reducing the Size of Speech DB for Corpus-based TTS Systems (코퍼스 기반 음성합성기의 데이터베이스 축소 방법)

  • 최승호;엄기완;강상기;김진영
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.8
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    • pp.703-710
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    • 2003
  • Because of their human-like synthesized speech quality, recently Corpus-Based Text-To-Speech(CB-TTS) have been actively studied worldwide. However, due to their large size speech database (DB), their application is very restricted. In this paper we propose and evaluate three DB reduction algorithms to which are designed to solve the above drawback. The first method is based on a K-means clustering approach, which selects k-representatives among multiple instances. The second method is keeping only those unit instances that are selected during synthesis, using a domain-restricted text as input to the synthesizer. The third method is a kind of hybrid approach of the above two methods and is using a large text as input in the system. After synthesizing the given sentences, the used unit instances and their occurrence information is extracted. As next step a modified K-means clustering is applied, which takes into account also the occurrence information of the selected unit instances, Finally we compare three pruning methods by evaluating the synthesized speech quality for the similar DB reduction rate, Based on perceptual listening tests, we concluded that the last method shows the best performance among three algorithms. More than this, the results show that the last method is able to reduce DB size without speech quality looses.

A study on the improvement of concrete defect detection performance through the convergence of transfer learning and k-means clustering (전이학습과 k-means clustering의 융합을 통한 콘크리트 결함 탐지 성능 향상에 대한 연구)

  • Younggeun Yoon;Taekeun Oh
    • The Journal of the Convergence on Culture Technology
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    • v.9 no.2
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    • pp.561-568
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    • 2023
  • Various defects occur in concrete structures due to internal and external environments. If there is a defect, it is important to efficiently identify and maintain it because there is a problem with the structural safety of concrete. However, recent deep learning research has focused on cracks in concrete, and studies on exfoliation and contamination are lacking. In this study, focusing on exfoliation and contamination, which are difficult to label, four models were developed and their performance evaluated through unlabelling method, filtering method, the convergence of transfer learning based k-means clustering. As a result of the analysis, the convergence model classified the defects in the most detail and could increase the efficiency compared to direct labeling. It is hoped that the results of this study will contribute to the development of deep learning models for various types of defects that are difficult to label in the future.

Design of Pattern Classification Rule based on Local Linear Discriminant Analysis Classifier by using Differential Evolutionary Algorithm (차분진화 알고리즘을 이용한 지역 Linear Discriminant Analysis Classifier 기반 패턴 분류 규칙 설계)

  • Roh, Seok-Beom;Hwang, Eun-Jin;Ahn, Tae-Chon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.22 no.1
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    • pp.81-86
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    • 2012
  • In this paper, we proposed a new design methodology of a pattern classification rule based on the local linear discriminant analysis expanded from the generic linear discriminant analysis which is used in the local area divided from the whole input space. There are two ways such as k-Means clustering method and the differential evolutionary algorithm to partition the whole input space into the several local areas. K-Means clustering method is the one of the unsupervised clustering methods and the differential evolutionary algorithm is the one of the optimization algorithms. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods.

Adaptive k-means clustering for Flying Ad-hoc Networks

  • Raza, Ali;Khan, Muhammad Fahad;Maqsood, Muazzam;Haider, Bilal;Aadil, Farhan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.6
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    • pp.2670-2685
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    • 2020
  • Flying ad-hoc networks (FANETs) is a vibrant research area nowadays. This type of network ranges from various military and civilian applications. FANET is formed by micro and macro UAVs. Among many other problems, there are two main issues in FANET. Limited energy and high mobility of FANET nodes effect the flight time and routing directly. Clustering is a remedy to handle these types of problems. In this paper, an efficient clustering technique is proposed to handle routing and energy problems. Transmission range of FANET nodes is dynamically tuned accordingly as per their operational requirement. By optimizing the transmission range packet loss ratio (PLR) is minimized and link quality is improved which leads towards reduced energy consumption. To elect optimal cluster heads (CHs) based on their fitness we use k-means. Selection of optimal CHs reduce the routing overhead and improves energy consumption. Our proposed scheme outclasses the existing state-of-the-art techniques, ACO based CACONET and PSO based CLPSO, in terms of energy consumption and cluster building time.

Opera Clustering: K-means on librettos datasets

  • Jeong, Harim;Yoo, Joo Hun
    • Journal of Internet Computing and Services
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    • v.23 no.2
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    • pp.45-52
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    • 2022
  • With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.