An efficient pattern growth approach for mining fault tolerant frequent itemsets
نویسندگان
چکیده
منابع مشابه
Frequent Itemsets Mining: An Efficient Graphical Approach
Recent advances in computer technology in terms of speed, cost, tremendous amount of computing power and decrease data processing time has spurred increased interest in data mining applications to extract useful knowledge from data. Over the last couple of years, data mining technology has been successfully employed to various business domains and scientific areas. Various data mining technique...
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The increasing importance of data stream arising in a wide range of advanced applications has led to the extensive study of mining frequent patterns. Mining data streams poses many new challenges amongst which are the one-scan nature, the unbounded memory requirement and the high arrival rate of data streams. In this paper, we propose a new approach for mining itemsets on data stream. Our appro...
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In this paper, an algorithm, called VB-FT-Mine (Vectors-Based Fault–Tolerant frequent patterns Mining), is proposed for mining fault-tolerant frequent patterns efficiently. In this approach, fault–tolerant appearing vectors are designed to represent the distribution that the candidate patterns contained in data sets with fault-tolerance. VB-FT-Mine algorithm applies depth-first pattern growing ...
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Itemset share has been proposed to evaluate the significance of itemsets for mining association rules in databases. The Fast Share Measure (FSM) algorithm is one of the best algorithms to discover all share-frequent itemsets efficiently. However, FSM is fast only when dealing with small datasets. In this study, we shall propose a revised version of FSM, called the Enhanced FSM (EFSM) algorithm ...
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ژورنال
عنوان ژورنال: Expert Systems with Applications
سال: 2020
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2019.113046