Updating generalized association rules with evolving taxonomies

Tseng, Ming-Cheng; Lin, Wen-Yang; Jeng, Rong

doi:10.1007/s10489-007-0096-5

Cited by 7 publications

(2 citation statements)

References 12 publications

(6 reference statements)

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“…This property says that if a pattern is infrequent, then all of its super patterns must be infrequent. The Apriori [3,4] algorithm is the initial solution of frequent pattern mining problem and very useful in association rule mining [3,4,18,25,28,30,32]. However, it suffers from the level-wise candidate generation-and-test problem and needs several database scans.…”

Section: Frequent Pattern Miningmentioning

confidence: 99%

HUC-Prune: an efficient candidate pruning technique to mine high utility patterns

et al. 2009

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Traditional frequent pattern mining methods consider an equal profit/weight for all items and only binary occurrences (0/1) of the items in transactions. High utility pattern mining becomes a very important research issue in data mining by considering the non-binary frequency values of items in transactions and different profit values for each item. However, most of the existing high utility pattern mining algorithms suffer in the level-wise candidate generation-and-test problem and generate too many candidate patterns. Moreover, they need several database scans which are directly dependent on the maximum candidate length. In this paper, we present a novel tree-based candidate pruning technique, called HUC-Prune (High Utility Candidates Prune), to solve these problems. Our technique uses a novel tree structure, called HUC-tree (High Utility Candidates tree), to capture important utility information of the candidate patterns. HUC-Prune avoids the level-wise candidate generation process by adopting a pattern growth approach. In contrast to the existing algorithms, its number of database scans is completely independent of the maximum candidate length. Extensive experimental results show that our algorithm is very efficient for high utility pattern mining and it outperforms the existing algorithms.

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Section: Frequent Pattern Miningmentioning

confidence: 99%

HUC-Prune: an efficient candidate pruning technique to mine high utility patterns

et al. 2009

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“…Claudia Marinica proposed a new approach to prune and filter discovered rules. Using domain Ontologies, the integration of user knowledge in the post-processing task was strengthened [6] .Ming-Cheng Tseng considered the problem of mining association rules with Ontological information and devised two efficient algorithms,called AROC and AROS,for discovering Ontological associations that exploit not only classification but also composition relationship between items [7,8,9] . Zahra Farzanyar proposed a approach that improves efficiency in the classical fuzzy association rule mining problem by providing the capability to handle domain Ontology relationships [10] .Andrea Bellandi proposed an integrated framework for extracting constraint-based multi-level association rules with an Ontology support [11] .…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Association Rules Mining Algorithm Based on Semantic Relativity

Zhang

Wang

2011

KEM

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Traditional multi-level association rules mining approaches are based only on database contents. The relations of items in itemset are considered rarely. It leads to generate a lot of meaningless itemsets. Aiming at the problem,multi-level association rules mining algorithm based on semantic relativity is proposed. Domain knowledge is described by Ontology. Every item is seen as a concept in Ontology. Semantic relativity is used to measure the semantic meaning of itemsets. Minimum support of itemset is set according to its length and semantic relativity. Semantic related minimum support with length-decrease is defined to filter meaningless itemsets. Experiments results showed that the method in the paper can improve the efficiency of multi-level association rules mining and generated meaningful rules.

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Updating generalized association rules with evolving fuzzy taxonomies

Lin

Tseng

2011

Soft Comput

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Mining generalized association rules with fuzzy taxonomic structures has been recognized as an important extension of generalized associations mining problem. To date most work on this problem, however, required the taxonomies to be static, ignoring the fact that the taxonomies of items cannot necessarily be kept unchanged. For instance, some items may be reclassified from one hierarchy tree to another for more suitable classification, abandoned from the taxonomies if they will no longer be produced, or added into the taxonomies as new items. Additionally, the membership degrees expressing the fuzzy classification may also need to be adjusted. Under these circumstances, effectively updating the discovered generalized association rules is a crucial task. In this paper, we examine this problem and propose two novel algorithms, called FDiff_ET and FDiff_ET*, to update the discovered generalized frequent itemsets. Empirical evaluations show that our algorithms can maintain their performance even in high degree of taxonomy evolution, and are significantly faster than applying the contemporary fuzzy generalized association mining algorithm FGAR to the database with evolving taxonomy.

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Updating generalized association rules with evolving taxonomies

Cited by 7 publications

References 12 publications

HUC-Prune: an efficient candidate pruning technique to mine high utility patterns

HUC-Prune: an efficient candidate pruning technique to mine high utility patterns

Multi-Level Association Rules Mining Algorithm Based on Semantic Relativity

Updating generalized association rules with evolving fuzzy taxonomies

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