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## Mining colossal frequent patterns by core pattern fusion (2007)

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Venue: | In Proc. 2007 Int. Conf. Data Engineering (ICDE’07 |

Citations: | 21 - 3 self |

### Citations

3611 | Fast algorithms for mining association rules
- Agrawal, Srikant
- 1994
(Show Context)
Citation Context ... decade. A pattern is frequent if and only if it occurs in at least σ fraction of a dataset, where σ is userdefined. It is essential to a broad range of applications including association rule mining =-=[2, 14]-=-, time-related process and scientific sequence data analysis, bioinfomatics, classification, indexing and clustering. Intense research on this topic has produced a series of mining algorithms for find... |

3329 | A.: Mining association rules between sets of items in large databases
- Agrawal, Imieliński, et al.
- 1993
(Show Context)
Citation Context ...lated Work LCM_maximal Top−k Pattern−Fusion 29 28 27 26 25 24 23 Minimum Support Threshold Figure 10. Run Time on ALL Frequent itemset mining, initiated by the introduction of association rule mining =-=[1]-=-, has been extensively studied [2, 17, 10, 4, 3, 11, 22, 13]. Efficient implementations appeared in the FIMI workshops. Most of the well studied frequent pattern mining algorithms, including Apriori [... |

1751 | Mining frequent patterns without candidate generation
- Han, Pei, et al.
- 2000
(Show Context)
Citation Context ...ons or recommendations expressed here are those of the authors and do not necessarily reflect the views of the funding agencies. frequent patterns in large databases of itemsets, sequences and graphs =-=[16, 22, 11]-=-. For many applications, these algorithms have proved to be effective. Efficient open source implementations were also available over years. For example, FPClose [8] and LCM2 [18] (an improved version... |

615 | Dynamic itemset counting and implication rules for market basket data,”
- Brin, Motwani, et al.
- 1997
(Show Context)
Citation Context ...rn−Fusion 29 28 27 26 25 24 23 Minimum Support Threshold Figure 10. Run Time on ALL Frequent itemset mining, initiated by the introduction of association rule mining [1], has been extensively studied =-=[2, 17, 10, 4, 3, 11, 22, 13]-=-. Efficient implementations appeared in the FIMI workshops. Most of the well studied frequent pattern mining algorithms, including Apriori [2], FP-growth [11], and CARPENTER [15], mine the complete se... |

470 | Sampling large databases for association rules”,
- Toivonen
- 1996
(Show Context)
Citation Context ...rn−Fusion 29 28 27 26 25 24 23 Minimum Support Threshold Figure 10. Run Time on ALL Frequent itemset mining, initiated by the introduction of association rule mining [1], has been extensively studied =-=[2, 17, 10, 4, 3, 11, 22, 13]-=-. Efficient implementations appeared in the FIMI workshops. Most of the well studied frequent pattern mining algorithms, including Apriori [2], FP-growth [11], and CARPENTER [15], mine the complete se... |

457 | Efficiently mining long patterns from databases.
- Bayardo
- 1998
(Show Context)
Citation Context ...plications, these algorithms have proved to be effective. Efficient open source implementations were also available over years. For example, FPClose [8] and LCM2 [18] (an improved version of MaxMiner =-=[3]-=-) published in 2003 and 2004 Frequent Itemset Mining Implementations Workshop (FIMI) can report the complete set of frequent itemsets in a few seconds for reasonably large data sets. However, the freq... |

410 | L.: Discovering frequent closed itemsets for association rules
- Pasquier, Bastide, et al.
- 1999
(Show Context)
Citation Context ...ons or recommendations expressed here are those of the authors and do not necessarily reflect the views of the funding agencies. frequent patterns in large databases of itemsets, sequences and graphs =-=[16, 22, 11]-=-. For many applications, these algorithms have proved to be effective. Efficient open source implementations were also available over years. For example, FPClose [8] and LCM2 [18] (an improved version... |

320 | CHARM: an efficient algorithm for closed itemset mining. In:
- MJ, CJ
- 2002
(Show Context)
Citation Context ...ons or recommendations expressed here are those of the authors and do not necessarily reflect the views of the funding agencies. frequent patterns in large databases of itemsets, sequences and graphs =-=[16, 22, 11]-=-. For many applications, these algorithms have proved to be effective. Efficient open source implementations were also available over years. For example, FPClose [8] and LCM2 [18] (an improved version... |

313 | Experiments of the effectiveness of dataflow- and controlflow-based test adequacy criteria.
- Hutchins, Foster, et al.
- 1994
(Show Context)
Citation Context ...gn Real data set 1: Replace. Replace is a program trace data set collected from the “replace” program, which is one of the Siemens Programs that have been widely used in software engineering research =-=[12]-=-. The program calls and transitions of 4,395 correct executions are recorded. Each type of program calls and transitions is considered as one item. There are 66 different program calls and transition... |

309 | MAFIA: a maximal frequent itemset algorithm for transactional databases,”
- Burdick, Calimlim, et al.
- 2001
(Show Context)
Citation Context ...blem. Extensive studies have proposed fast algorithms for mining frequent closed itemsets, such as Aclose [16], CHARM [22] and CLOSET+ [20], and maximum closed itemsets, such as, Max-Miner [3], MAFIA =-=[5]-=- and GenMax[7]. In all of these studies, the mining of the complete pattern set becomes the major task. While in many applications, there exist an explosive number of closed or maximum patterns, none ... |

237 | E cient algorithms for discovering association rules
- Mannila, Toivonen, et al.
- 1994
(Show Context)
Citation Context ... decade. A pattern is frequent if and only if it occurs in at least σ fraction of a dataset, where σ is userdefined. It is essential to a broad range of applications including association rule mining =-=[2, 14]-=-, time-related process and scientific sequence data analysis, bioinfomatics, classification, indexing and clustering. Intense research on this topic has produced a series of mining algorithms for find... |

185 | CLOSET+: Searching for the best strategies for mining frequent closed itemsets,
- Wang, Han, et al.
- 2003
(Show Context)
Citation Context ...quent itemsets [9, 3] can partially alleviate this redundancy problem. Extensive studies have proposed fast algorithms for mining frequent closed itemsets, such as Aclose [16], CHARM [22] and CLOSET+ =-=[20]-=-, and maximum closed itemsets, such as, Max-Miner [3], MAFIA [5] and GenMax[7]. In all of these studies, the mining of the complete pattern set becomes the major task. While in many applications, ther... |

180 | Efficiently using prefix-trees in mining frequent itemsets. FIMI’03
- Grahne, Zhu
- 2003
(Show Context)
Citation Context ...ets, sequences and graphs [16, 22, 11]. For many applications, these algorithms have proved to be effective. Efficient open source implementations were also available over years. For example, FPClose =-=[8]-=- and LCM2 [18] (an improved version of MaxMiner [3]) published in 2003 and 2004 Frequent Itemset Mining Implementations Workshop (FIMI) can report the complete set of frequent itemsets in a few second... |

161 | M.J.: Efficiently mining maximal frequent itemsets. In: ICDM,
- Gouda, Zaki
- 2001
(Show Context)
Citation Context ...e studies have proposed fast algorithms for mining frequent closed itemsets, such as Aclose [16], CHARM [22] and CLOSET+ [20], and maximum closed itemsets, such as, Max-Miner [3], MAFIA [5] and GenMax=-=[7]-=-. In all of these studies, the mining of the complete pattern set becomes the major task. While in many applications, there exist an explosive number of closed or maximum patterns, none of the existin... |

83 | Mining compressed frequent-pattern sets
- Xin, Han, et al.
- 2005
(Show Context)
Citation Context ...Consequently, it motivates us to solve the following problem: How to efficiently find a good approximation to the colossal frequent patterns? There have been some recent work on pattern summarization =-=[21]-=- focusing on post-processing of the complete mining result in order to give a compact answer set. These approaches do not apply for our problem as we intend to avoid the generation of the complete min... |

77 | Data mining, hypergraph transversals, and machine learning.
- Gunopulos, Khardon, et al.
- 1997
(Show Context)
Citation Context ...quent itemset is frequent. This well-known downward closure property leads to an explosive number of frequent patterns. The introduction of closed frequent itemsets [16] and maximal frequent itemsets =-=[9, 3]-=- partially alleviated this redundancy problem. A frequent pattern is closed if and only if a super-pattern with the same support does not exist. A frequent pattern is maximal if and only if it does no... |

75 | CARPENTER: Finding Closed Patterns in Long Biological Datasets, Proceeding of SIGKDD
- Pan, Cong, et al.
- 2003
(Show Context)
Citation Context ... 17, 10, 4, 3, 11, 22, 13]. Efficient implementations appeared in the FIMI workshops. Most of the well studied frequent pattern mining algorithms, including Apriori [2], FP-growth [11], and CARPENTER =-=[15]-=-, mine the complete set of frequent itemsets. 22 21sAccording to the Apriori property, any subset of a frequent itemset is frequent. This downward closure property leads to an explosive number of freq... |

54 | Mining Frequent Item Sets by Opportunistic Projection
- Liu, Pan, et al.
(Show Context)
Citation Context ...rn−Fusion 29 28 27 26 25 24 23 Minimum Support Threshold Figure 10. Run Time on ALL Frequent itemset mining, initiated by the introduction of association rule mining [1], has been extensively studied =-=[2, 17, 10, 4, 3, 11, 22, 13]-=-. Efficient implementations appeared in the FIMI workshops. Most of the well studied frequent pattern mining algorithms, including Apriori [2], FP-growth [11], and CARPENTER [15], mine the complete se... |

53 | Discovering All Most Specific Sentences by Randomized Algorithms
- Gunopulos, Mannila, et al.
- 1997
(Show Context)
Citation Context |

44 | Mining top-k covering rule groups for gene expression data. In:
- Cong, Tan, et al.
- 2005
(Show Context)
Citation Context ...ent patterns, are explosive in size. It comes with no surprise that this phenomenon should fail all mining algorithms which attempt to report the complete answer set. Take one microarray dataset, ALL =-=[6]-=-, for example, which contains 38 transactions each with 866 items. Our experiments show that, when given a low support threshold of 10, FPClose, LCM2 and TFP (top-k) [19] all failed to complete execut... |

41 | LCM ver. 2: Efficient mining algorithms for frequent/closed/maximal itemsets.
- Uno, Kiyomi, et al.
- 2005
(Show Context)
Citation Context ...s and graphs [16, 22, 11]. For many applications, these algorithms have proved to be effective. Efficient open source implementations were also available over years. For example, FPClose [8] and LCM2 =-=[18]-=- (an improved version of MaxMiner [3]) published in 2003 and 2004 Frequent Itemset Mining Implementations Workshop (FIMI) can report the complete set of frequent itemsets in a few seconds for reasonab... |

31 | Tfp: an efficient algorithm for mining top-k frequent closed itemsets.
- Wang, Han, et al.
- 2005
(Show Context)
Citation Context ...ke one microarray dataset, ALL [6], for example, which contains 38 transactions each with 866 items. Our experiments show that, when given a low support threshold of 10, FPClose, LCM2 and TFP (top-k) =-=[19]-=- all failed to complete execution. More importantly, mining tasks in practice usually attach much greater importance to patterns that are larger in pattern size, e.g., longer sequences are usually of ... |