HYBRID: An efficient unifying process to mine frequent itemsets

机译：HYBRID：高效的统一流程来挖掘频繁项集

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Current advancement in technology inexorably leads to data flood. More data is generated from banking, telecom, scientific experiments, etc. Data mining is the process of extracting useful information from this flooded data, which helps in making profitable future decisions in these fields. Frequent itemset mining is one of the focus research areas and an important step to fin association rules. Time and space requirements for generating frequent itemsets are of utter importance. Algorithms to mine frequent itemsets effectively help in finding association rules and also help in many other data mining tasks. In this paper, an efficient hybrid algorithm was designed using a unifying process of the algorithms Improved Apriori and FP-Growth. Results indicate that the proposed hybrid algorithm, albeit more complex, consumes fewer memory resources and faster execution time.

机译：当前技术的发展不可避免地导致数据泛滥。从银行，电信，科学实验等产生了更多的数据。数据挖掘是从这些泛洪数据中提取有用信息的过程，这有助于在这些领域做出有利可图的未来决策。频繁项集挖掘是重点研究领域之一，也是查找关联规则的重要步骤。生成频繁项集的时间和空间要求非常重要。挖掘频繁项集的算法有效地帮助找到关联规则，并且还帮助许多其他数据挖掘任务。在本文中，使用改进的Apriori算法和FP-Growth算法的统一过程设计了一种有效的混合算法。结果表明，所提出的混合算法尽管更复杂，却消耗更少的内存资源和更快的执行时间。

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来源
《2017 IEEE 3rd International Conference on Engineering Technologies and Social Sciences》|2017年|1-5|共5页
会议地点 Bangkok(TH)
作者
Nurul Fariza Zulkurnain; Ahmad Shah;
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作者单位

Electrical and Computer Engineering, International Islamic University Malaysia, Selangor, Malaysia;

Electrical and Computer Engineering, International Islamic University Malaysia, Selangor, Malaysia;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Itemsets; Data mining; Memory management; Layout; Partitioning algorithms; Task analysis;

机译：项目集;数据挖掘;内存管理;布局;分区算法;任务分析;;

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专利

1. EFFICIENT SUBSET-LATTICE ALGORITHMS FOR MINING CLOSED FREQUENT ITEMSETS AND MAXIMAL FREQUENT ITEMSETS IN DATA STREAMS [J] . Ye-In Chang, Chia-En Li, Wei-Hau Peng, International Journal of Electrical Engineering: Transactions of the Chinese Institute of Engineers, Series E . 2013,第2期

机译：高效的子格算法，用于挖掘数据流中的封闭频率项和最大频率项
2. On the Efficient Representation of Datasets as Graphs to Mine Maximal Frequent Itemsets [J] . Halim Zahid, Ali Omer, Khan Muhammad Ghufran IEEE Transactions on Knowledge and Data Engineering . 2021,第4期

机译：关于数据集的高效表示为挖掘最大频繁项集的图表
3. AT-Mine: An Efficient Algorithm of Frequent Itemset Mining on Uncertain Dataset [J] . Le Wang, Lin Feng, Mingfei Wu Journal of Computers . 2013,第6期

机译：at-mine：在不确定数据集中的频繁替代项目集的高效算法
4. HYBRID: An efficient unifying process to mine frequent itemsets [C] . Nurul Fariza Zulkurnain, Ahmad Shah IEEE International Conference on Engineering Technologies and Social Sciences . 2017

机译：杂交：频繁的唯一项目集的高效统一过程
5. Efficiently mining frequent itemsets from very large databases. [D] . Zhu, Jianfei. 2004

机译：从大型数据库中有效地挖掘频繁的项目集。
6. An efficient pattern growth approach for mining fault tolerant frequent itemsets [O] . Shariq Bashir -1

机译：挖掘容错频繁项集的有效模式增长方法
7. Exploiting the Duality of Maximal Frequent Itemsets and Minimal Infrequent Itemsets for I/O Efficient Association Rule Mining [O] . K. K. Loo, Chi-lap Yip, Ben Kao, 2000

机译：利用最大频繁项集和最小频繁项集的对偶性进行I / O有效关联规则挖掘

HYBRID: An efficient unifying process to mine frequent itemsets

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