{"title":"SOCEN-CLUSTER:一种新的快速聚类技术","authors":"Farouk Baccari, M. Sayadi","doi":"10.1109/ICEESA.2013.6578367","DOIUrl":null,"url":null,"abstract":"In this paper, we present a novel clustering technique for unindexed, randomized, multidimensional, datasets. The main advantage of the proposed technique is the time and space complexity that were reduced to linear cardinality dependency. The algorithmic implementation shown in this paper uses some heuristics to enhance the overall execution time and space required making them fully scalable. This particularity makes it easier for ASICS / FPGA architects to implement such a technique in a constrained environment.","PeriodicalId":212631,"journal":{"name":"2013 International Conference on Electrical Engineering and Software Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SOCEN-CLUSTER: A new rapid clustering technique\",\"authors\":\"Farouk Baccari, M. Sayadi\",\"doi\":\"10.1109/ICEESA.2013.6578367\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a novel clustering technique for unindexed, randomized, multidimensional, datasets. The main advantage of the proposed technique is the time and space complexity that were reduced to linear cardinality dependency. The algorithmic implementation shown in this paper uses some heuristics to enhance the overall execution time and space required making them fully scalable. This particularity makes it easier for ASICS / FPGA architects to implement such a technique in a constrained environment.\",\"PeriodicalId\":212631,\"journal\":{\"name\":\"2013 International Conference on Electrical Engineering and Software Applications\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-03-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Electrical Engineering and Software Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEESA.2013.6578367\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Electrical Engineering and Software Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEESA.2013.6578367","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we present a novel clustering technique for unindexed, randomized, multidimensional, datasets. The main advantage of the proposed technique is the time and space complexity that were reduced to linear cardinality dependency. The algorithmic implementation shown in this paper uses some heuristics to enhance the overall execution time and space required making them fully scalable. This particularity makes it easier for ASICS / FPGA architects to implement such a technique in a constrained environment.