{"title":"A-Close+:一种挖掘频繁闭项集的算法","authors":"M. Shekofteh, A. M. Rahmani, M. A. Dezfuli","doi":"10.1109/ICACTE.2008.135","DOIUrl":null,"url":null,"abstract":"Association Rule Mining (ARM) is the most essential technique for data mining that mines hidden associations between data in large databases. The most important function of ARM is to find frequent itemsets. Frequent closed itemsets (FCI) is an important condense representation method for frequent itemsets, and because of its importance in recent years, there have been many algorithms implemented for it. One of the most fundamental algorithms for frequent closed itemset is A-close. In this paper, we optimize this algorithm using both optimized techniques \"reducing pruning time\" and \"reducing database size\", called \"A-close+\"..Results show that the performance cost of our algorithm is considerably less than A-close.","PeriodicalId":364568,"journal":{"name":"2008 International Conference on Advanced Computer Theory and Engineering","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A-Close+: An Algorithm for Mining Frequent Closed Itemsets\",\"authors\":\"M. Shekofteh, A. M. Rahmani, M. A. Dezfuli\",\"doi\":\"10.1109/ICACTE.2008.135\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Association Rule Mining (ARM) is the most essential technique for data mining that mines hidden associations between data in large databases. The most important function of ARM is to find frequent itemsets. Frequent closed itemsets (FCI) is an important condense representation method for frequent itemsets, and because of its importance in recent years, there have been many algorithms implemented for it. One of the most fundamental algorithms for frequent closed itemset is A-close. In this paper, we optimize this algorithm using both optimized techniques \\\"reducing pruning time\\\" and \\\"reducing database size\\\", called \\\"A-close+\\\"..Results show that the performance cost of our algorithm is considerably less than A-close.\",\"PeriodicalId\":364568,\"journal\":{\"name\":\"2008 International Conference on Advanced Computer Theory and Engineering\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Conference on Advanced Computer Theory and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACTE.2008.135\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Advanced Computer Theory and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACTE.2008.135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A-Close+: An Algorithm for Mining Frequent Closed Itemsets
Association Rule Mining (ARM) is the most essential technique for data mining that mines hidden associations between data in large databases. The most important function of ARM is to find frequent itemsets. Frequent closed itemsets (FCI) is an important condense representation method for frequent itemsets, and because of its importance in recent years, there have been many algorithms implemented for it. One of the most fundamental algorithms for frequent closed itemset is A-close. In this paper, we optimize this algorithm using both optimized techniques "reducing pruning time" and "reducing database size", called "A-close+"..Results show that the performance cost of our algorithm is considerably less than A-close.