Mining Frequent Itemsets in Evolving Databases

Veloso, Adriano; Meira, Wagner; Carvalho, Mário de Faria; Pôssas, Bruno; Parthasarathy, Srinivasan; Zaki, Mohammed J.

doi:10.1137/1.9781611972726.29

Cited by 66 publications

(49 citation statements)

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“…STUM is evaluated with various degrees of support threshold adjustment. STUM is compared with some state-of-the-art frequent pattern discovery and maintenance methods, including GC-growth [12], ZIGZAG [15] and Border [2]. GCgrowth is an effective algorithm that generates frequent generators.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Negative Generator Border for Effective Pattern Maintenance

Feng

Wong

et al.

Advanced Data Mining and Applications

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Abstract. In this paper, we study the maintenance of frequent patterns in the context of the generator representation. The generator representation is a concise and lossless representation of frequent patterns. We effectively maintain the generator representation by systematically expanding its Negative Generator Border. According to our literature review, no prior work has studied the maintenance of the generator representation. To illustrate the proposed maintenance idea, a new algorithm is developed to maintain the generator representation for support threshold adjustment. Our experimental results show that the proposed algorithm is significantly faster than other state-of-the-art algorithms. This proposed maintenance idea can also be extended to other representations of frequent patterns as demonstrated in this paper.

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Section: Experimental Evaluationmentioning

confidence: 99%

“…Due to this strict constraint, the performance of Moment degrades dramatically when the amount of updates gets large. ZIGZAG [15] is another example, which effectively maintains the maximal patterns [3]. ZIGZAG updates the maximal patterns by a backtracking search, which is guided by the outcomes of the previous maintenance iteration.…”

Section: Introductionmentioning

confidence: 99%

Negative Generator Border for Effective Pattern Maintenance

Feng

Wong

et al.

Advanced Data Mining and Applications

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“…The Tid-tree is developed based on the concept of Transaction Identifier List, in short Tid-list. The Tid-list is very popular in the literature of data mining [8,13]. Tidlists, serve as the vertical projections of items, greatly facilitate the discovery of frequent itemsets and their support.…”

Section: Implementation Techniquesmentioning

confidence: 99%

“…These algorithms includes ZIGZAG [13], FpClose [9] and GC-growth [11]. ZIGZAG is one of the most recently proposed algorithms, which also addresses the maintenance of frequent patterns when transactions are removed.…”

Section: Experimental Studiesmentioning

confidence: 99%

“…Since the Tid linked list can only be removed one by one, the computational complexity of TRUM is O(|D dec |), where |D dec | denotes the size of the decremental dataset. TRUM is much more computationally effective, compared to previous works, like [8,13], whose computational complexity is O(N F P ), where N F P refers to the number of frequent patterns. This is because O(|D dec |) N F P .…”

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confidence: 99%

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Evolution and Maintenance of Frequent Pattern Space When Transactions Are Removed

Feng

Dong

et al.

Advances in Knowledge Discovery and Data Mining

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Abstract. This paper addresses the maintenance of discovered frequent patterns when a batch of transactions are removed from the original dataset. We conduct an in-depth investigation on how the frequent pattern space evolves under transaction removal updates using the concept of equivalence classes. Inspired by the evolution analysis, an effective and exact algorithm TRUM is proposed to maintain frequent patterns. TRUM maintains frequent patterns efficiently by updating only the affected equivalence classes. Experimental results demonstrate that our algorithm outperforms representative state-of-the-art algorithms.

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Constrained itemset mining on a sequence of incoming data blocks

Baralis

Cerquitelli

Chiusano

2010

Int. J. Intell. Syst.

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Many real-life databases are updated by means of incoming business information. In these databases (e.g., transactional data from large retail chains, call-detail records), the content evolves through periodical insertions (or deletions) of data blocks. Since data evolve over time, algorithms have to be devised to incrementally update data mining models. This paper presents a novel index, called I-Forest, to support itemset mining on incoming data blocks, where new blocks are inserted periodically, or old blocks are discarded. The I-Forest structure provides a complete data representation and allows different kind of analyses (e.g., investigate quarterly data), besides supporting user-defined time and support constraints. The I-Forest index has been implemented into the PostgreSQL open source DBMS and exploits its physical level access methods. Experiments, run for both sparse and dense data distributions, show the effectiveness of the I-Forest-based approach to perform itemset mining with both time and support constraints. The execution time of the I-Forest-based itemset mining technique is often faster than the Prefix-Tree algorithm accessing static data on flat files. C 2010 Wiley Periodicals, Inc.

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Mining Frequent Itemsets in Evolving Databases

Cited by 66 publications

References 0 publications

Negative Generator Border for Effective Pattern Maintenance

Negative Generator Border for Effective Pattern Maintenance

Evolution and Maintenance of Frequent Pattern Space When Transactions Are Removed

Constrained itemset mining on a sequence of incoming data blocks

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