• 제목, 요약, 키워드: Clustering

검색결과 5,183건 처리시간 0.041초

K-means Clustering for Environmental Indicator Survey Data

  • Park, Hee-Chang;Cho, Kwang-Hyun
    • 한국데이터정보과학회:학술대회논문집
    • /
    • /
    • pp.185-192
    • /
    • 2005
  • There are many data mining techniques such as association rule, decision tree, neural network analysis, clustering, genetic algorithm, bayesian network, memory-based reasoning, etc. We analyze 2003 Gyeongnam social indicator survey data using k-means clustering technique for environmental information. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper, we used k-means clustering of several clustering techniques. The k-means clustering is classified as a partitional clustering method. We can apply k-means clustering outputs to environmental preservation and environmental improvement.

  • PDF

Empirical Comparisons of Clustering Algorithms using Silhouette Information

  • Jun, Sung-Hae;Lee, Seung-Joo
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.10 no.1
    • /
    • pp.31-36
    • /
    • 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.

Environmental Survey Data Modeling Using K-means Clustering Techniques

  • Park, Hee-Chang;Cho, Kwang-Hyun
    • Journal of the Korean Data and Information Science Society
    • /
    • v.16 no.3
    • /
    • pp.557-566
    • /
    • 2005
  • Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper we used k-means clustering of several clustering techniques. The k-means Clustering Is classified as a partitional clustering method. We analyze 2002 Gyeongnam social indicator survey data using k-means clustering techniques for environmental information. We can use these outputs given by k-means clustering for environmental preservation and environmental improvement.

  • PDF

클러스터링 기법을 이용한 수용가별 전력 데이터 패턴 분석 (Customer Load Pattern Analysis using Clustering Techniques)

  • 유승형;김홍석;오도은;노재구
    • KEPCO Journal on Electric Power and Energy
    • /
    • v.2 no.1
    • /
    • pp.61-69
    • /
    • 2016
  • Understanding load patterns and customer classification is a basic step in analyzing the behavior of electricity consumers. To achieve that, there have been many researches about clustering customers' daily load data. Nowadays, the deployment of advanced metering infrastructure (AMI) and big-data technologies make it easier to study customers' load data. In this paper, we study load clustering from the view point of yearly and daily load pattern. We compare four clustering methods; K-means clustering, hierarchical clustering (average & Ward's method) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). We also discuss the relationship between clustering results and Korean Standard Industrial Classification that is one of possible labels for customers' load data. We find that hierarchical clustering with Ward's method is suitable for clustering load data and KSIC can be well characterized by daily load pattern, but not quite well by yearly load pattern.

Environmental Survey Data Modeling using K-means Clustering Techniques

  • 박희창;조광현
    • 한국데이터정보과학회:학술대회논문집
    • /
    • /
    • pp.77-86
    • /
    • 2004
  • Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper we used k-means clustering of several clustering techniques. The k-means Clustering is classified as a partitional clustering method. We analyze 2002 Gyeongnam social indicator survey data using k-means clustering techniques for environmental information. We can use these outputs given by k-means clustering for environmental preservation and environmental improvement.

  • PDF

최적에 가까운 군집화를 위한 이단계 방법 (A Two-Stage Method for Near-Optimal Clustering)

  • 윤복식
    • 한국경영과학회지
    • /
    • v.29 no.1
    • /
    • pp.43-56
    • /
    • 2004
  • The purpose of clustering is to partition a set of objects into several clusters based on some appropriate similarity measure. In most cases, clustering is considered without any prior information on the number of clusters or the structure of the given data, which makes clustering is one example of very complicated combinatorial optimization problems. In this paper we propose a general-purpose clustering method that can determine the proper number of clusters as well as efficiently carry out clustering analysis for various types of data. The method is composed of two stages. In the first stage, two different hierarchical clustering methods are used to get a reasonably good clustering result, which is improved In the second stage by ASA(accelerated simulated annealing) algorithm equipped with specially designed perturbation schemes. Extensive experimental results are given to demonstrate the apparent usefulness of our ASA clustering method.

Medoid Determination in Deterministic Annealing-based Pairwise Clustering

  • Lee, Kyung-Mi;Lee, Keon-Myung
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.11 no.3
    • /
    • pp.178-183
    • /
    • 2011
  • The deterministic annealing-based clustering algorithm is an EM-based algorithm which behaves like simulated annealing method, yet less sensitive to the initialization of parameters. Pairwise clustering is a kind of clustering technique to perform clustering with inter-entity distance information but not enforcing to have detailed attribute information. The pairwise deterministic annealing-based clustering algorithm repeatedly alternates the steps of estimation of mean-fields and the update of membership degrees of data objects to clusters until termination condition holds. Lacking of attribute value information, pairwise clustering algorithms do not explicitly determine the centroids or medoids of clusters in the course of clustering process or at the end of the process. This paper proposes a method to identify the medoids as the centers of formed clusters for the pairwise deterministic annealing-based clustering algorithm. Experimental results show that the proposed method locate meaningful medoids.

An Overview of Unsupervised and Semi-Supervised Fuzzy Kernel Clustering

  • Frigui, Hichem;Bchir, Ouiem;Baili, Naouel
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.13 no.4
    • /
    • pp.254-268
    • /
    • 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.

합성곱 오토인코더 기반의 응집형 계층적 군집 분석 (Agglomerative Hierarchical Clustering Analysis with Deep Convolutional Autoencoders)

  • 박노진;고한석
    • 한국멀티미디어학회논문지
    • /
    • v.23 no.1
    • /
    • pp.1-7
    • /
    • 2020
  • Clustering methods essentially take a two-step approach; extracting feature vectors for dimensionality reduction and then employing clustering algorithm on the extracted feature vectors. However, for clustering images, the traditional clustering methods such as stacked auto-encoder based k-means are not effective since they tend to ignore the local information. In this paper, we propose a method first to effectively reduce data dimensionality using convolutional auto-encoder to capture and reflect the local information and then to accurately cluster similar data samples by using a hierarchical clustering approach. The experimental results confirm that the clustering results are improved by using the proposed model in terms of clustering accuracy and normalized mutual information.

공간국부성을 최적화하는 클러스터링 방법 (A Clustering Method for Optimizing Spatial Locality)

  • 김홍기
    • 한국정보과학회논문지:데이타베이스
    • /
    • v.31 no.2
    • /
    • pp.83-90
    • /
    • 2004
  • 본 논문에서는 순환적인 검색공간과 장애물이 존재하는 검색공간에서 객체들을 클러스터링할 때 고려해야하는 CCD(Clustering with Circular Distance) 문제와 COD(Clustering with Obstructed Distance) 문제를 연구하였다. 그리고 다차원 검색공간에서 삽입이나 삭제가 빈번히 발생하는 객체들을 효율적으로 클러스터링하기 위한 새로운 클러스터링 알고리즘을 제안하였다. 제안한 클러스터링 알고리즘에는 CCD 및 COD 문제를 해결하기 위한 거리 함수가 정의된다. 그리고 최소의 연산 시간으로 높은 공간 국부성을 갖는 클러스터들을 생성하기 위한 클러스터링 방법이 포함된다.