{"title":"An implementation for dynamic combination of frequent items in HCFPMINETREE","authors":"A. Meenakshi, K. Alagarsamy","doi":"10.1109/ICCCNT.2012.6396074","DOIUrl":null,"url":null,"abstract":"Modern digital databases are immersed with massive collection of data. The proliferation, ubiquity and increasing power of computer technology have increased data collection and storage. As datasets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented in-direct, automatic data processing. There are so many existing algorithms to find frequent itemsets in Association Rule Mining. In this paper, we have modified FPtree algorithm as HCFPMine frequency of tree (Horizontal Compact Frequent Pattern Mining) combines all the maximum occurrence of frequent itemsets before converting into the tree structure. We have also used median value for finding same frequency of items and inserted it as node in the tree structure. We have explained it with an algorithm and illustrated with examples and also depicted the runtime and memory space for the construction of the tree structure.","PeriodicalId":364589,"journal":{"name":"2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT'12)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT'12)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCNT.2012.6396074","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Modern digital databases are immersed with massive collection of data. The proliferation, ubiquity and increasing power of computer technology have increased data collection and storage. As datasets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented in-direct, automatic data processing. There are so many existing algorithms to find frequent itemsets in Association Rule Mining. In this paper, we have modified FPtree algorithm as HCFPMine frequency of tree (Horizontal Compact Frequent Pattern Mining) combines all the maximum occurrence of frequent itemsets before converting into the tree structure. We have also used median value for finding same frequency of items and inserted it as node in the tree structure. We have explained it with an algorithm and illustrated with examples and also depicted the runtime and memory space for the construction of the tree structure.
现代数字数据库中充斥着海量的数据。计算机技术的普及、普及和日益增强的能力增加了数据的收集和存储。随着数据集的规模和复杂性的增长,直接动手的数据分析越来越多地被直接、自动的数据处理所增强。在关联规则挖掘中,现有的频繁项集查找算法很多。在本文中,我们将FPtree算法改进为HCFPMine frequency of tree (Horizontal Compact frequency Pattern Mining),将所有频繁项集的最大出现次数组合在一起,然后转换为树结构。我们还使用中值来查找相同频率的项目,并将其作为树结构中的节点插入。我们用一个算法解释了它,用例子说明了它,还描述了构建树形结构的运行时和内存空间。