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1,304
Efficiently Using Prefix-trees in Mining Frequent Itemsets
, 2003
"... Efficient algorithms for mining frequent itemsets are crucial for mining association rules. Methods for mining frequent itemsets and for iceberg data cube computation have been implemented using a prefix-tree structure, known as an FP-tree, for storing compressed information about frequent itemsets. ..."
Abstract
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Cited by 180 (1 self)
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Efficient algorithms for mining frequent itemsets are crucial for mining association rules. Methods for mining frequent itemsets and for iceberg data cube computation have been implemented using a prefix-tree structure, known as an FP-tree, for storing compressed information about frequent itemsets
An Improved Method for Frequent Itemset Mining
"... Abstract — Frequent itemset mining is an important step in association rule mining. Several algorithms have been proposed for efficient frequent itemset mining in transactional database. We present an improved approach to mine frequent itemset in transactional database. The algorithm idea is derived ..."
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Cited by 1 (0 self)
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Abstract — Frequent itemset mining is an important step in association rule mining. Several algorithms have been proposed for efficient frequent itemset mining in transactional database. We present an improved approach to mine frequent itemset in transactional database. The algorithm idea
Discovery of maximum length frequent itemsets
"... The use of frequent itemsets has been limited by the high computational cost as well as the large number of resulting itemsets. In many real-world scenarios, however, it is often sufficient to mine a small representative subset of frequent itemsets with low computational cost. To that end, in this p ..."
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The use of frequent itemsets has been limited by the high computational cost as well as the large number of resulting itemsets. In many real-world scenarios, however, it is often sufficient to mine a small representative subset of frequent itemsets with low computational cost. To that end
Hiding Co-Occurring Frequent Itemsets
"... Knowledge hiding, hiding rules/patterns that are inferable from published data and attributed sensitive, is extensively studied in the literature in the context of frequent itemsets and association rules mining from transactional data. The research in this thread is focused mainly on developing soph ..."
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Knowledge hiding, hiding rules/patterns that are inferable from published data and attributed sensitive, is extensively studied in the literature in the context of frequent itemsets and association rules mining from transactional data. The research in this thread is focused mainly on developing
Mining Frequent Itemsets with Normalized Weight
- in Continuous Data Streams, JIPS
"... Abstract—A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should ..."
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Cited by 4 (0 self)
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support the flexible trade-off between processing time and mining accuracy. In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets. In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent
Mining Distributed Frequent Itemset with Hadoop
"... Abstract: In the current scenario there has been growing attention in the area of distributed environment especially in data mining. Frequent pattern mining is active area of research in today’s scenario. In this paper a survey on frequent itemset mining with distributed environment has been present ..."
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Abstract: In the current scenario there has been growing attention in the area of distributed environment especially in data mining. Frequent pattern mining is active area of research in today’s scenario. In this paper a survey on frequent itemset mining with distributed environment has been
On Differentially Private Frequent Itemset Mining ∗
"... We consider differentially private frequent itemset mining. We begin by exploring the theoretical difficulty of simultaneously providing good utility and good privacy in this task. While our analysis proves that in general this is very difficult, itleaves aglimmer ofhopeinthatourproofofdifficulty re ..."
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Cited by 11 (1 self)
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We consider differentially private frequent itemset mining. We begin by exploring the theoretical difficulty of simultaneously providing good utility and good privacy in this task. While our analysis proves that in general this is very difficult, itleaves aglimmer ofhopeinthatourproofofdifficulty
Mining All Non-Derivable Frequent Itemsets
, 2002
"... Recent studies on frequent itemset mining algorithms resulted in significant performance improvements. However, if the minimal support threshold is set too low, or the data is highly correlated, the number of frequent itemsets itself can be prohibitively large. To overcome this problem, recently sev ..."
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Cited by 127 (12 self)
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Recent studies on frequent itemset mining algorithms resulted in significant performance improvements. However, if the minimal support threshold is set too low, or the data is highly correlated, the number of frequent itemsets itself can be prohibitively large. To overcome this problem, recently
Survey on Frequent Itemset Mining Algorithms
"... Many researchers invented ideas to generate the frequent itemsets. The time required for generating frequent itemsets plays an important role. Some algorithms are designed, considering only the time factor. Our study includes depth analysis of algorithms and discusses some problems of generating fre ..."
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Cited by 5 (0 self)
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Many researchers invented ideas to generate the frequent itemsets. The time required for generating frequent itemsets plays an important role. Some algorithms are designed, considering only the time factor. Our study includes depth analysis of algorithms and discusses some problems of generating
Hiding Sensitive Predictive Frequent Itemsets
"... Abstract—In this work, we propose an itemset hiding algorithm with four versions that use different heuristics in selecting the item in itemset and the transaction for distortion. The main strengths of itemset hiding algorithm can be stated as i) it works without pre-mining so privacy breech caused ..."
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by revealing frequent itemsets in advance is prevented and efficiency is increased, ii) base algorithm (Matrix-Apriori) works without candidate generation so efficiency is increased, iii) sanitized database and frequent itemsets of this database are given as outputs so no post-mining is required and iv) simple
Results 11 - 20
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1,304
