Maintenance of discovered association rules in large databases: an incremental updating technique

Cheung, David W.; Han, Jiawei; Ng, V.T.; Wong, Carlos K. H.

doi:10.1109/icde.1996.492094

Cited by 531 publications

(362 citation statements)

References 6 publications

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“…An incremental updating technique called FUP [20] was proposed for the maintenance of the association rules. This work was based on the level-wise candidate set generation-and-test methodology of the Apriori algorithm.…”

Section: Frequent Pattern Miningmentioning

confidence: 99%

Handling Dynamic Weights in Weighted Frequent Pattern Mining

Ahmed

Tanbeer

Jeong

et al. 2008

IEICE Transactions on Information and Systems

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SUMMARY Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights. key words: data mining, knowledge discovery, weighted frequent pattern mining, dynamic weight

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Section: Frequent Pattern Miningmentioning

confidence: 99%

Handling Dynamic Weights in Weighted Frequent Pattern Mining

Ahmed

Tanbeer

Jeong

et al. 2008

IEICE Transactions on Information and Systems

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“…To enhance the performance of algorithms of data mining, many researchers [1,2,3,4,5,6,7] have focused on increasingly updating association rules and sequential patterns. But if we update association rules or sequential patterns too frequently, the cost of computation will increase significantly.…”

Section: Introductionmentioning

confidence: 99%

When to Update the Sequential Patterns of Stream Data?

Zheng

2003

Advances in Knowledge Discovery and Data Mining

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In this paper, we first define a difference measure between the old and new sequential patterns of stream data, which is proved to be a distance. Then we propose an experimental method, called TPD (Tradeoff between Performance and Difference), to decide when to update the sequential patterns of stream data by making a tradeoff between the performance of increasingly updating algorithms and the difference of sequential patterns. The experiments for the incremental updating algorithm IUS on two data sets show that generally, as the size of incremental windows grows, the values of the speedup and the values of the difference will decrease and increase respectively. It is also shown experimentally that the incremental ratio determined by the TPD method does not monotonically increase or decrease but changes in a range between 20 and 30 percentage for the IUS algorithm.

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“…Methods falling into that category that have been studied in the context of frequent itemset discovery are: incremental mining [7], caching intermediate query results [17], and reusing materialized complete [5][15] [16] or condensed [13] results of previous queries provided that syntactic differences between the queries satisfy certain conditions.…”

Section: Related Workmentioning

confidence: 99%

“…A significant amount of research on efficient processing of frequent itemset queries has been done in recent years, focusing mainly on constraint handling (see [18] for an overview) and reusing results of previous queries [5] [7][15] [16].…”

Section: Introductionmentioning

confidence: 99%

Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth

Wojciechowski

Galecki

Gawronek

Lecture Notes in Computer Science

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Abstract. Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. Recently, a new problem of optimizing processing of sets of frequent itemset queries has been considered and two multiple query optimization techniques for frequent itemset queries: Mine Merge and Common Counting have been proposed and tested on the Apriori algorithm. In this paper we discuss and experimentally evaluate three strategies for concurrent processing of frequent itemset queries using FP-growth as a basic frequent itemset mining algorithm. The first strategy is Mine Merge, which does not depend on a particular mining algorithm and can be applied to FP-growth without modifications. The second is an implementation of the general idea of Common Counting for FP-growth. The last is a completely new strategy, motivated by identified shortcomings of the previous two strategies in the context of FP-growth.

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Maintenance of discovered association rules in large databases: an incremental updating technique

Cited by 531 publications

References 6 publications

Handling Dynamic Weights in Weighted Frequent Pattern Mining

Handling Dynamic Weights in Weighted Frequent Pattern Mining

When to Update the Sequential Patterns of Stream Data?

Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth

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