Book ChapterDOI
A tree-based approach for frequent pattern mining from uncertain data
Carson K. Leung,Mark Anthony F. Mateo,Dale A. Brajczuk +2 more
- pp 653-661
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TLDR
A tree-based mining algorithm is proposed to efficiently find frequent patterns from uncertain data, where each item in the transactions is associated with an existential probability.Abstract:
Many frequent pattern mining algorithms find patterns from traditional transaction databases, in which the content of each transaction--namely, items--is definitely known and precise. However, there are many real-life situations in which the content of transactions is uncertain. To deal with these situations, we propose a tree-based mining algorithm to efficiently find frequent patterns from uncertain data, where each item in the transactions is associated with an existential probability. Experimental results show the efficiency of our proposed algorithm.read more
Citations
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Proceedings ArticleDOI
Frequent pattern mining with uncertain data
TL;DR: This paper will show how broad classes of algorithms can be extended to the uncertain data setting, and study candidate generate-and-test algorithms, hyper-structure algorithms and pattern growth based algorithms.
Posted Content
Mining Frequent Itemsets over Uncertain Databases
TL;DR: In this paper, the authors provide baseline implementations of eight representative algorithms and test their performances with uniform measures fairly, according to the fair tests over many different benchmark data sets and discuss some new findings.
Journal ArticleDOI
Mining frequent itemsets over uncertain databases
TL;DR: This paper verifies that the two definitions of the frequent itemset have a tight connection and can be unified together when the size of data is large enough and provides baseline implementations of eight existing representative algorithms and test their performances with uniform measures fairly.
Proceedings ArticleDOI
Mining of Frequent Itemsets from Streams of Uncertain Data
Carson K. Leung,Boyu Hao +1 more
TL;DR: Two tree-based mining algorithms are proposed to efficiently find frequent itemsets from streams of uncertain data, where each item in the transactions in the streams is associated with an existential probability.
Journal ArticleDOI
Efficient algorithms for mining high-utility itemsets in uncertain databases
TL;DR: A novel framework, named potential high-utility itemset mining (PHUIM) in uncertain databases, is proposed to efficiently discover not only the itemset with high utilities but also the itemsets with high existence probabilities in an uncertain database based on the tuple uncertainty model.
References
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Proceedings Article
Fast algorithms for mining association rules
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Fast Algorithms for Mining Association Rules in Large Databases
Journal ArticleDOI
Mining frequent patterns without candidate generation
Jiawei Han,Jian Pei,Yiwen Yin +2 more
TL;DR: This study proposes a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develops an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth.
Proceedings Article
CLOSET : An Efficient Algorithm for Mining Frequent Closed Itemsets
TL;DR: An e cient algorithm, CLOSET, for mining closed itemsets is proposed, with the development of three techniques: applying a compressed, frequent pattern tree FP-tree structure for miningclosed itemsets without candidate generation, and developing a single pre x path compression technique to identify frequent closed itemset quickly.
Proceedings ArticleDOI
Exploratory mining and pruning optimizations of constrained associations rules
TL;DR: An architecture that opens up the black-box, and supports constraint-based, human-centered exploratory mining of associations, and introduces and analyzes two properties of constraints that are critical to pruning: anti-monotonicity and succinctness.