The document summarizes the minutes from a POPONG meeting on July 9, 2011. It discusses analyzing Korean parliamentary bill data using binary matrices to represent legislator participation in bills. It then performs similarity calculations and hierarchical clustering on the data to group legislators based on similarity in their legislative participation patterns across six different datasets.

1. POPONG meeting (2011-07-09)
작성자: 박은정, 박주성
1. Raw data
A. 국회 의안 정보 시스템
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- 제 18 대 국회 의안 중 처리 의안
2. Data preprocessing (박주성)
A. 의원 별 참여 의안에 대한 binary matrix 구성
(단, 의안 이름이 같은데 발의자 구성이 같은 경우에는, 하나의 record 로 간
주)
의안 1 의안 2 의안 3 … 의안 m
의원 1 1 0 0 … 1
의원 2 1 0 1 … 0
… … … … … …
의원 n 0 1 0 … 1
B. Case 분류 및 중복제거: 총 6 가지 case 에 대한 분석
의원수(n) 의안수(m)
All (D1) 329 3262
Accepted (D3) 329 505
Rejected (D5) 329 2757
All + 노이즈제거
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(D2) 329 2856
Accepted + 노이즈제거 (D4) 329 472
Rejected + 노이즈제거 (D6) 329 2384
(Spotfire ‘BillMemberAnalysis_0709.dxp’ 파일 참고)
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http://likms.assembly.go.kr/bill/jsp/main.jsp
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공동발의자수가 150 명 이상인 경우를 제외
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2. 3. Data Analysis (박은정)
A. Similarity calculation: 세 가지 set similarity measure 사용
B. Similarity Based Hierarchical Clustering
- 의원 간의 상관계수를 계산하여 그들간의비유사성(dissimilarity) 계산
Dissimilarity = 1 - Abs(Similarity)
- 위에서 계산된 비유사성을 이용하여 계층군집 알고리즘 적용
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※ 계층군집 알고리즘
• 각 개체 간의 비유사도를 기반으로 가장 가까운 것 두 개를 묶는다.
• 묶은 개체 둘은 다시 하나의 개체로 간주하여 다시 앞 과정을 반복한다.
• 앞 두 과정을 전체가 하나로 묶일 때까지 반복한다.
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Single linkage(minimum distance) 사용
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