{"title":"SOCK:一个社会计量分析系统","authors":"R. Alba, M. Gutmann","doi":"10.1145/1103251.1103252","DOIUrl":null,"url":null,"abstract":"The SOCK system is intended to provide a set of procedures for sociometric clique identification. The procedures in SOCK are based upon numerical clustering techniques which isolate highly interrelated subsets of individuals. Since clustering techniques do not generally work well when operating on simple dichotomous choice data, i.e., on adjacency matrices, these procedures involve the derivation of some matrix of pairwise social distance or proximity from the dichotomous choice data.","PeriodicalId":129356,"journal":{"name":"ACM Sigsoc Bulletin","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"SOCK: a sociometric analysis system\",\"authors\":\"R. Alba, M. Gutmann\",\"doi\":\"10.1145/1103251.1103252\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The SOCK system is intended to provide a set of procedures for sociometric clique identification. The procedures in SOCK are based upon numerical clustering techniques which isolate highly interrelated subsets of individuals. Since clustering techniques do not generally work well when operating on simple dichotomous choice data, i.e., on adjacency matrices, these procedures involve the derivation of some matrix of pairwise social distance or proximity from the dichotomous choice data.\",\"PeriodicalId\":129356,\"journal\":{\"name\":\"ACM Sigsoc Bulletin\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Sigsoc Bulletin\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1103251.1103252\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Sigsoc Bulletin","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1103251.1103252","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The SOCK system is intended to provide a set of procedures for sociometric clique identification. The procedures in SOCK are based upon numerical clustering techniques which isolate highly interrelated subsets of individuals. Since clustering techniques do not generally work well when operating on simple dichotomous choice data, i.e., on adjacency matrices, these procedures involve the derivation of some matrix of pairwise social distance or proximity from the dichotomous choice data.