Mining frequent patterns without candidate generation

@inproceedings{Han2000MiningFP,
  title={Mining frequent patterns without candidate generation},
  author={Jiawei Han and Jian Pei and Yiwen Yin},
  booktitle={SIGMOD '00},
  year={2000}
}
Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. In this study, we propose a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for… 
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
TLDR
A novel frequent-pattern tree (FP-tree) structure is proposed, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and an efficient FP-tree-based mining method, FP-growth, is developed for mining the complete set of frequent patterns by pattern fragment growth.
MINING IN FREQUENT PATTERN USING EFFICIENT PATTERN-GROWTH METHODS
Mining frequent patterns from large databases plays an essential role in many data mining tasks and has broad applications. Most of the previously proposed methods adopt apriori-like
Pattern-growth methods for frequent pattern mining
Mining frequent patterns from large databases plays an essential role in many data mining tasks and has broad applications. Most of the previously proposed methods adopt apriori-like
Mining frequent patterns by pattern-growth: methodology and implications
TLDR
It is shown that frequent pattern growth is efficient at mining large databases and its further development may lead to scalable mining of many other kinds of patterns as well.
Frequent Item Sets in Large Uncertain Databases are mined efficiently
In recent year, mining frequent itemsets over uncertain databases and computing statistical information on probabilistic data under the Possible World Semantics (PWS) and maintaining the result for
A frequent pattern mining algorithm based on FP-growth without generating tree
TLDR
An algorithm to generate frequent patterns without generating a tree is introduced and it is shown that this algorithm will improve the time complexity and memory complexity of the database as well.
The Frequent Pattern List: Another Framework for Mining Frequent Patterns
TLDR
This paper proposes a simpler and more efficient data structure for representing the databases --- the Frequent Pattern List (FPL), which is able to partition both the search space and the solution space so that a divide-and-conquer approach can be applied in mining frequent patterns.
Mining Maximal Patterns Based on Improved FP-tree and Array Technique
  • Hua-jin Wang, Chun-an Hu
  • Computer Science
    2010 Third International Symposium on Intelligent Information Technology and Security Informatics
  • 2010
TLDR
An efficient algorithm for mining maximal frequent patterns based on improved FP-tree and array technique, called IAFP-max, is presented, which outperforms most exiting algorithms MAFIA, GenMax and FPmax.
Mining sequential patterns by pattern-growth: the PrefixSpan approach
TLDR
This paper proposes a projection-based, sequential pattern-growth approach for efficient mining of sequential patterns, and shows that PrefixSpan outperforms the a priori-based algorithm GSP, FreeSpan, and SPADE and is the fastest among all the tested algorithms.
SQL based frequent pattern mining without candidate generation
TLDR
This study presents an evaluation of SQL based frequent pattern mining with a novel frequent pattern growth (FP-growth) method, which is efficient and scalable for mining both long and short patterns without candidate generation.
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