• 제목/요약/키워드: Clustering Problem

검색결과 708건 처리시간 0.024초

비디오 데이터베이스 구축을 위하여 장면전환 검출과 샷 클러스터링을 이용한 비디오 개요 추출 (Video Abstracting Using Scene Change Detection and Sho Clustering for Construction of Efficient Video Database)

  • 표성배
    • 한국컴퓨터정보학회논문지
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    • 제7권4호
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    • pp.75-82
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    • 2002
  • 대부분의 비디오는 대용량의 장시간 데이터로서 비디오 시청자들이 전반적인 내용을 이해하기에는 충분하지 못하다 본 논문에서는 이러한 문제점을 해결하기 위하여 효율적인 장면 전환 검출 방법과 새로운 샷 클러스터링을 이용한 비디오 개요 추출 방법을 제시한다. 장면전환 검출 방법은 컬러 히스토그램과 χ2 히스토그램을 합성한 방법을 이용하여 추출하도록 한다. 클러스터링은 지역적 히스토그램의 차이 값을 이용한 유사성 측정과 새로운 샷 병합 알고리즘을 통해 수행하도록 한다 또한 실제 TV방송 프로그램을 대상으로 비디오 개요 추출 실험 결과를 제시한다.

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Initial Mode Decision Method for Clustering in Categorical Data

  • Yang, Soon-Cheol;Kang, Hyung-Chang;Kim, Chul-Soo
    • Journal of the Korean Data and Information Science Society
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    • 제18권2호
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    • pp.481-488
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    • 2007
  • The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. The k-modes algorithm is to extend the k-means paradigm to categorical domains. The algorithm requires a pre-setting or random selection of initial points (modes) of the clusters. This paper improved the problem of k-modes algorithm, using the Max-Min method that is a kind of methods to decide initial values in k-means algorithm. we introduce new similarity measures to deal with using the categorical data for clustering. We show that the mushroom data sets and soybean data sets tested with the proposed algorithm has shown a good performance for the two aspects(accuracy, run time).

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집단화를 위한 병렬 알고리즘의 구현 (Parallel Algorithm For Level Clustering)

  • 배용근
    • 한국정보처리학회논문지
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    • 제2권2호
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    • pp.148-155
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    • 1995
  • 많은 양의 패턴들을 분석할 때, 이 패턴들을 어떤 평가함수에 의해서 여러 군으로 집단화할 필요가 있다. 이 과정은 입력 패턴의 수가 많을 경우 상당한 량의 계산을 필 요로 하며, 이를 위한 병렬화 알고리즘이 요구된다. 이 문제를 해결하기 위하여 본 논 문은 K-means 알고리즘을 병렬화한 병렬 집단화 알고리즘을 제안하고, 메세지 전송을 근간으로 하는 MIMD 병렬 컴퓨터하에서 이를 수행하였다. 실험 및 성능 분석을 통하여 입력 패턴이 많을 경우, 본 병렬 알고리즘이 적절함을 알 수 있었다.

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A Study of Association Rule Mining by Clustering through Data Fusion

  • Cho, Kwang-Hyun;Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
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    • 제18권4호
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    • pp.927-935
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    • 2007
  • Currently, Gyeongnam province is executing the social index survey every year to the provincials. But, this survey has the limit of the analysis as execution of the different survey per 3 year cycles. The solution of this problem is data fusion. Data fusion is the process of combining multiple data in order to provide information of tactical value to the user. But, data fusion doesn#t mean the ultimate result. Therefore, efficient analysis for the data fusion is also important. In this study, we present data fusion method of statistical survey data. Also, we suggest application methodology of association rule mining by clustering through data fusion of statistical survey data.

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A novel Neuro Fuzzy Modeling using Gaussian Mixture Models

  • Kim, Sung-Suk;Kwak, Keun-Chang;Kim, Sung-Soo;Chun, Myung-Geun;Ryu, Jeong-Woong
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2002년도 ICCAS
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    • pp.110.1-110
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    • 2002
  • We propose a novel neuro-fuzzy system based on an efficient clustering method. It is a very useful method that improves the performance of a fuzzy model with small number of fuzzy rules. The fuzzy clustering methods are studied in the wide range of fuzzy modeling. One of them, the grid partition method has problem of exponentially increasing number of rules when the dimension of input or number of membership function is linearly increased. On the other hand, the Expectation Maximization algorithm is an efficient estimation for unknown parameters of the Gaussian mixture model. Here it is noted that the parameters can be used for fuzzy clustering method. In a fuzzy modeling, it is desired that...

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Design of Hard Partition-based Non-Fuzzy Neural Networks

  • Park, Keon-Jun;Kwon, Jae-Hyun;Kim, Yong-Kab
    • International journal of advanced smart convergence
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    • 제1권2호
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    • pp.30-33
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    • 2012
  • This paper propose a new design of fuzzy neural networks based on hard partition to generate the rules of the networks. For this we use hard c-means (HCM) clustering algorithm. The premise part of the rules of the proposed networks is realized with the aid of the hard partition of input space generated by HCM clustering algorithm. The consequence part of the rule is represented by polynomial functions. And the coefficients of the polynomial functions are learned by BP algorithm. The number of the hard partition of input space equals the number of clusters and the individual partitioned spaces indicate the rules of the networks. Due to these characteristics, we may alleviate the problem of the curse of dimensionality. The proposed networks are evaluated with the use of numerical experimentation.

Multiple Person Tracking based on Spatial-temporal Information by Global Graph Clustering

  • Su, Yu-ting;Zhu, Xiao-rong;Nie, Wei-Zhi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제9권6호
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    • pp.2217-2229
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    • 2015
  • Since the variations of illumination, the irregular changes of human shapes, and the partial occlusions, multiple person tracking is a challenging work in computer vision. In this paper, we propose a graph clustering method based on spatio-temporal information of moving objects for multiple person tracking. First, the part-based model is utilized to localize individual foreground regions in each frame. Then, we heuristically leverage the spatio-temporal constraints to generate a set of reliable tracklets. Finally, the graph shift method is applied to handle tracklet association problem and consequently generate the completed trajectory for individual object. The extensive comparison experiments demonstrate the superiority of the proposed method.

개미군 최적화 방법을 적용한 무선 센서 네트워크에서의 클러스터링 최적 설계 (Clustering Optimal Design in Wireless Sensor Network using Ant Colony Optimization)

  • 김성수;최승현
    • 경영과학
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    • 제26권3호
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    • pp.55-65
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    • 2009
  • The objective of this paper is to propose an ant colony optimization (ACO) for clustering design in wireless sensor network problem. This proposed ACO approach is designed to deal with the dynamics of the sensor nodes which can be adaptable to topological changes to any network graph in a time. Long communication distances between sensors and a sink in a sensor network can greatly consume the energy of sensors and reduce the lifetime of a network. We can greatly minimize the total communication distance while minimizing the number of cluster heads using proposed ACO. Simulation results show that our proposed method is very efficient to find the best solutions comparing to the optimal solution using CPLEX in 100, 200, and 400 node sensor networks.

Reinterpretation of Multiple Correspondence Analysis using the K-Means Clustering Analysis

  • Choi, Yong-Seok;Hyun, Gee Hong;Kim, Kyung Hee
    • Communications for Statistical Applications and Methods
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    • 제9권2호
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    • pp.505-514
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    • 2002
  • Multiple correspondence analysis graphically shows the correspondent relationship among categories in multi-way contingency tables. It is well known that the proportions of the principal inertias as part of the total inertia is low in multiple correspondence analysis. Moreover, although this problem can be overcome by using the Benzecri formula, it is not enough to show clear correspondent relationship among categories (Greenacre and Blasius, 1994, Chapter 10). In addition, they show that Andrews' plot is useful in providing the correspondent relationship among categories. However, this method also does not give some concise interpretation among categories when the number of categories is large. Therefore, in this study, we will easily interpret the multiple correspondence analysis by applying the K-means clustering analysis.

Clustering and Communications Scheduling in WSNs using Mixed Integer Linear Programming

  • Avril, Francois;Bernard, Thibault;Bui, Alain;Sohier, Devan
    • Journal of Communications and Networks
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    • 제16권4호
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    • pp.421-429
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    • 2014
  • We consider the problem of scheduling communications in wireless sensor networks (WSNs) to ensure battery preservation through the use of the sleeping mode of sensors.We propose a communication protocol for 1-hop WSNs and extend it to multi-hop WSNs through the use of a 1-hop clustering algorithm.We propose to schedule communications in each cluster in a virtual communication ring so as to avoid collisions. Since clusters are cliques, only one sensor can speak or listen in a cluster at a time, and all sensors need to speak in each of their clusters at least once to realize the communication protocol. We model this situation as a mathematical program.