• Title/Summary/Keyword: clustering technique

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Medoid Determination in Deterministic Annealing-based Pairwise Clustering

  • Lee, Kyung-Mi;Lee, Keon-Myung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.11 no.3
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    • pp.178-183
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    • 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.

K-means Clustering for Environmental Indicator Survey Data

  • Park, Hee-Chang;Cho, Kwang-Hyun
    • 한국데이터정보과학회:학술대회논문집
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    • 2005.04a
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    • pp.185-192
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    • 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.

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An Efficient Conceptual Clustering Scheme (효율적인 개념 클러스터링 기법)

  • Yang, Gi-Chul
    • Journal of Korea Entertainment Industry Association
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    • v.14 no.4
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    • pp.349-354
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    • 2020
  • This paper, firstly, propose a new Clustering scheme Based on Conceptual graphs (CBC) that can describe objects freely and can perform clustering efficiently. The conceptual clustering is one of machine learning technique. The similarity among the objects in conceptual clustering are decided on the bases of concept membership, unlike the general clustering scheme which decide the similarity without considering the context or environment of the objects. A new conceptual clustering scheme, CBC, which can perform efficient conceptual clustering by describing various objects freely with conceptual graphs is introduced in this paper.

A Cluster Group Head Selection using Trajectory Clustering Technique (궤적 클러스터링 기법을 이용한 클러스터 그룹 헤드 선정)

  • Kim, Jin-Su;Shin, Seung-Soo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.12 no.12
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    • pp.5865-5872
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    • 2011
  • Multi-hop communication in clustering system is the technique that forms the cluster to aggregate the sensing data and transmit them to base station through midway cluster head. Cluster head around base station send more packet than that of far from base station. Because of this hot spot problem occurs and cluster head around base station increases energy consumption. In this paper, I propose a cluster group head selection using trajectory clustering technique(CHST). CHST select cluster head and group head using trajectory clustering technique and fitness function and it increases the energy efficiency. Hot spot problem can be solved by selection of cluster group with multi layer and balanced energy consumption using it's fitness function. I also show that proposed CHST is better than previous clustering method at the point of network energy efficiency.

Evolutionary Computation-based Hybird Clustring Technique for Manufacuring Time Series Data (제조 시계열 데이터를 위한 진화 연산 기반의 하이브리드 클러스터링 기법)

  • Oh, Sanghoun;Ahn, Chang Wook
    • Smart Media Journal
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    • v.10 no.3
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    • pp.23-30
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    • 2021
  • Although the manufacturing time series data clustering technique is an important grouping solution in the field of detecting and improving manufacturing large data-based equipment and process defects, it has a disadvantage of low accuracy when applying the existing static data target clustering technique to time series data. In this paper, an evolutionary computation-based time series cluster analysis approach is presented to improve the coherence of existing clustering techniques. To this end, first, the image shape resulting from the manufacturing process is converted into one-dimensional time series data using linear scanning, and the optimal sub-clusters for hierarchical cluster analysis and split cluster analysis are derived based on the Pearson distance metric as the target of the transformation data. Finally, by using a genetic algorithm, an optimal cluster combination with minimal similarity is derived for the two cluster analysis results. And the performance superiority of the proposed clustering is verified by comparing the performance with the existing clustering technique for the actual manufacturing process image.

Two Phase Hierarchical Clustering Algorithm for Group Formation in Data Mining (데이터 마이닝에서 그룹 세분화를 위한 2단계 계층적 글러스터링 알고리듬)

  • 황인수
    • Korean Management Science Review
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    • v.19 no.1
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    • pp.189-196
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    • 2002
  • Data clustering is often one of the first steps in data mining analysis. It Identifies groups of related objects that can be used as a starling point for exploring further relationships. This technique supports the development of population segmentation models, such as demographic-based customer segmentation. This paper Purpose to present the development of two phase hierarchical clustering algorithm for group formation. Applications of the algorithm for product-customer group formation in customer relationahip management are also discussed. As a result of computer simulations, suggested algorithm outperforms single link method and k-means clustering.

Clustering Algorithms for Reducing Energy Consumption - A Review

  • Kinza Mubasher;Rahat Mansha
    • International Journal of Computer Science & Network Security
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    • v.23 no.7
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    • pp.109-118
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    • 2023
  • Energy awareness is an essential design flaw in wireless sensor network. Clustering is the most highly regarded energy-efficient technique that offers various benefits such as energy efficiency and network lifetime. Clusters create hierarchical WSNs that introduce the efficient use of limited sensor node resources and thus enhance the life of the network. The goal of this paper is to provide an analysis of the various energy efficient clustering algorithms. Analysis is based on the energy efficiency and network lifetime. This review paper provides an analysis of different energy-efficient clustering algorithms for WSNs.

Functional hierarchical clustering using shape distance

  • Kyungmin Ahn
    • Communications for Statistical Applications and Methods
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    • v.31 no.5
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    • pp.601-612
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    • 2024
  • A functional clustering analysis is a crucial machine learning technique in functional data analysis. Many functional clustering methods have been developed to enhance clustering performance. Moreover, due to the phase variability between functions, elastic functional clustering methods, such as applying the Fisher-Rao metric, which can manage phase variation during clustering, have been developed to improve model performance. However, aligning functions without considering the phase variation can distort functional information because phase variation can be a natural characteristic of functions. Hence, we propose a state-of-the-art functional hierarchical clustering that can manage phase and amplitude variations of functional data. This approach is based on the phase and amplitude separation method using the norm-preserving time warping of functions. Due to its invariance property, this representation provides robust variability for phase and amplitude components of functions and improves clustering performance compared to conventional functional hierarchical clustering models. We demonstrate this framework using simulated and real data.

Fuzzy Technique-based Identification of Close and Distant Clusters in Clustering

  • Lee, Kyung-Mi;Lee, Keon-Myung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.11 no.3
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    • pp.165-170
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    • 2011
  • Due to advances in hardware performance, user-friendly interfaces are becoming one of the major concerns in information systems. Linguistic conversation is a very natural way of human communications. Fuzzy techniques have been employed to liaison the discrepancy between the qualitative linguistic terms and quantitative computerized data. This paper deals with linguistic queries using clustering results on data sets, which are intended to retrieve the close clusters or distant clusters from the clustering results. In order to support such queries, a fuzzy technique-based method is proposed. The method introduces distance membership functions, namely, close and distant membership functions which transform the metric distance between two objects into the degree of closeness or farness, respectively. In order to measure the degree of closeness or farness between two clusters, both cluster closeness measure and cluster farness measure which incorporate distance membership function and cluster memberships are considered. For the flexibility of clustering, fuzzy clusters are assumed to be formed. This allows us to linguistically query close or distant clusters by constructing fuzzy relation based on the measures.

A Clustering Method Considering the Threshold of Energy Consumption Model in Wireless Sensor Networks (무선 센서 네트워크에서 에너지 소모 모델의 임계값을 고려한 클러스터링 기법)

  • Kim, Jin-Su
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.11 no.10
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    • pp.3950-3957
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    • 2010
  • Wireless sensor network is composed of sensor node with limited sources, and to maintain and repair is vexatious once made up. Accordingly it is important matter to maximize the network lifetime by minimizing the energy consumption in wireless sensor network, and utilizing the limited sources efficiently. In this paper, I propose a technique arranging the cluster number with efficiency in clustering method to optimize the energy consumption. The energy usage needed for wireless transmission varies in distance(threshold). This technique reduces the energy consumption considering the threshold when arranging the cluster number. I verify that the clustering method organized through the valid processes outperform the LEACH(Low-Energy Adaptive Clustering Hierarchy) in total energy consumption.