• Title/Summary/Keyword: Ward의 최소분산법

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Selection of Optimal Variables for Clustering of Seoul using Genetic Algorithm (유전자 알고리즘을 이용한 서울시 군집화 최적 변수 선정)

  • Kim, Hyung Jin;Jung, Jae Hoon;Lee, Jung Bin;Kim, Sang Min;Heo, Joon
    • Journal of Korean Society for Geospatial Information Science
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    • v.22 no.4
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    • pp.175-181
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    • 2014
  • Korean government proposed a new initiative 'government 3.0' with which the administration will open its dataset to the public before requests. City of Seoul is the front runner in disclosure of government data. If we know what kind of attributes are governing factors for any given segmentation, these outcomes can be applied to real world problems of marketing and business strategy, and administrative decision makings. However, with respect to city of Seoul, selection of optimal variables from the open dataset up to several thousands of attributes would require a humongous amount of computation time because it might require a combinatorial optimization while maximizing dissimilarity measures between clusters. In this study, we acquired 718 attribute dataset from Statistics Korea and conducted an analysis to select the most suitable variables, which differentiate Gangnam from other districts, using the Genetic algorithm and Dunn's index. Also, we utilized the Microsoft Azure cloud computing system to speed up the process time. As the result, the optimal 28 variables were finally selected, and the validation result showed that those 28 variables effectively group the Gangnam from other districts using the Ward's minimum variance and K-means algorithm.

Studies on Differentiation of a Paddy Weed, Bur Beggarticks(Bidens tripartita L.) (논 잡초(雜草) 가막사리(Bidens tripartita L.) 생태종(生態種)의 분화(分化)에 관(關)한 연구(硏究))

  • Kim, Myung-Hyun;Rho, Yeong-Deok
    • Korean Journal of Weed Science
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    • v.17 no.3
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    • pp.303-309
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    • 1997
  • Variation of morphological and physiological traits of 50 Bidens tripartita accessions were studied and the accessions were grouped through cluster analysis based on four major characters; plant type, leaf partition, achene length, days to flowering. Bidens tripartite accessions have shown significant variations in plant type, stem length, days to flowering, leaf shape, leaf partition, chlorophyll content, leaf color, stem color, achene color, achene length and achene shape. Most of Bidens tripartite accessions appeared to have strong dormancy and also photodormancy with some exceptions. Plants could be classified into 5 types from straight(I) to triangle(V), and intermediate diamond type(III) was prevalent. The plant type score has negative correlation with the stem length. None, three, and five part leaved plants were observed and most of them were three or five parted. Leaf partition had negative correlation with achene length and chlorophyll content. Average days to flowering was 108 days in the range of 94~141 days. It had positive correlation with achene length and leaf shape and negative correlation with achene color. Average achene length was 10.0mm and it had positive correlation with achene shape, stem length, days to flowering and leaf shape. It also had negative correlation with leaf color, stem color, achene color, leaf partition. Bidens tripartite accessions could be divided into identifiable six groups from the cluster analysis at the distance 0.06 using Ward's minimum-variance method.

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