{"title":"应用Prefixed-Itemset和压缩矩阵优化Hadoop基于mapreduce的Apriori算法","authors":"Ruiqi Sun, Yuqiang Li","doi":"10.1145/3384544.3384610","DOIUrl":null,"url":null,"abstract":"Apriori algorithm is the classical algorithm for mining association rules. However, it also has some problems, such as comparing the identical itemset repeatedly and scanning the external storage database frequently. Based on the previous research, this paper proposed a method of applying the prefixed-itemset and the compression matrix to optimize the connection step, pruning step, support counting step and transaction storage mode of the Apriori algorithm. The experimental results show that compared with the conventional Apriori algorithm, the optimized Apriori algorithm has more powerful mining efficiency and more excellent performance.","PeriodicalId":200246,"journal":{"name":"Proceedings of the 2020 9th International Conference on Software and Computer Applications","volume":"201 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Applying Prefixed-Itemset and Compression Matrix to Optimize the MapReduce-based Apriori Algorithm on Hadoop\",\"authors\":\"Ruiqi Sun, Yuqiang Li\",\"doi\":\"10.1145/3384544.3384610\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Apriori algorithm is the classical algorithm for mining association rules. However, it also has some problems, such as comparing the identical itemset repeatedly and scanning the external storage database frequently. Based on the previous research, this paper proposed a method of applying the prefixed-itemset and the compression matrix to optimize the connection step, pruning step, support counting step and transaction storage mode of the Apriori algorithm. The experimental results show that compared with the conventional Apriori algorithm, the optimized Apriori algorithm has more powerful mining efficiency and more excellent performance.\",\"PeriodicalId\":200246,\"journal\":{\"name\":\"Proceedings of the 2020 9th International Conference on Software and Computer Applications\",\"volume\":\"201 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2020 9th International Conference on Software and Computer Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3384544.3384610\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 9th International Conference on Software and Computer Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3384544.3384610","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Applying Prefixed-Itemset and Compression Matrix to Optimize the MapReduce-based Apriori Algorithm on Hadoop
Apriori algorithm is the classical algorithm for mining association rules. However, it also has some problems, such as comparing the identical itemset repeatedly and scanning the external storage database frequently. Based on the previous research, this paper proposed a method of applying the prefixed-itemset and the compression matrix to optimize the connection step, pruning step, support counting step and transaction storage mode of the Apriori algorithm. The experimental results show that compared with the conventional Apriori algorithm, the optimized Apriori algorithm has more powerful mining efficiency and more excellent performance.
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