Mining interesting imperfectly sporadic rules

Koh, Yun Sing; Rountree, Nathan; O’Keefe, Richard A.

doi:10.1007/s10115-007-0074-6

Cited by 24 publications

(4 citation statements)

References 16 publications

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“…FP-growth, AprioriTid, Sporadic and Inverse algorithms are similar to Apriori; however, FP-growth generates a pattern by constructing a FP tree to reduce computational complexity. Sporadic and Inverse algorithms obtain both frequent and rate rules by setting a variable support threshold (Koh et al, 2008).…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Markov logic network method for reconstructing association rule-mining tasks in library book recommendation

Wang

Feng

et al. 2021

IDD

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Purpose This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation. Design/methodology/approach The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets. Findings The method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields. Research limitations/implications The time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future. Originality/value The authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.

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Section: Experiments and Resultsmentioning

confidence: 99%

A Markov logic network method for reconstructing association rule-mining tasks in library book recommendation

Wang

Feng

et al. 2021

IDD

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“…For instance, Laros (2005) looks for a substring, as short as possible, that appears exactly once in a set of strings. With regard to relational outliers, several definitions and corresponding algorithms for anomalous pattern discovery can be found: sporadic rules (Koh & Rountree, 2005;Koh et al, 2008) (rules with low support but high confidence), minimal infrequent itemsets (Haglin & Manning, 2007) and unexpected rules (Plantevit et al, 2007) (with a support between two thresholds). Exception rules (Suzuki, 2002) refer to the extension of the premise of a 'common sense rule', refuting the consequence of that rule.…”

Section: Relational Outliersmentioning

confidence: 99%

Outlier detection in relational data: A case study in geographical information systems

Maervoet¹,

Vens²,

Berghe³

et al. 2012

Expert Systems with Applications

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Geographical information systems are commonly used for a variety of purposes. Many of them make use of a large database of geographical data, the correctness of which strongly influences the reliability of the system. In this paper, we present an approach to quality maintenance that is based on automatic discovery of non-perfect regularities in the data. The underlying idea is that exceptions to these regularities ('outliers') are considered probable errors in the data, to be investigated by a human expert. A case study shows how the tool can be used for extracting valuable knowledge about outliers in real-world geographical data, in an adaptive manner to the evolving data model supporting it. While the tool aims specifically at geographical information systems, the underlying approach is more broadly applicable for quality maintenance in data-rich intelligent systems.

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“…C¡c h÷îng nghi ¶n cùu ch½nh t¼m c¡c luªt k¸t hñp hi¸m ÷ñc giîi thi»u trong [24]. Theo h÷îng ti¸p cªn sû döng ÷íng bi ¶n ph¥n chia giúa c¡c tªp, c¡c t¡c gi£ trong [4,5] ÷a ra kh¡i ni»m luªt sporadic (luªt k¸t hñp hi¸m hay cán gåi l luªt hi¸m) v ph¥n chia luªt sporadic th nh hai lo¤i l : luªt sporadic tuy»t èi v luªt sporadic khaeng tuy»t èi. V §n • t¼m c¡c tªp sporadic tuy»t èi cho luªt k¸t hñp sporadic tuy»t èi v• cì b£n ¢ ÷ñc gi£i quy¸t tr ¶n c£ CSDL t¡c vö v CSDL ành l÷ñng.…”

Section: Giîi Thiu Chungunclassified

“…• xu §t ¦u ti ¶n v• t¼m c¡c luªt sporadic khaeng tuy»t èi ÷ñc Koh Y. S. v cëng sü giîi thi»u trong [5]. Luªt sporadic khaeng tuy»t èi vîi ë hé trñ cüc tiºu maxSup v ë tin cªy cüc tiºu minConf l c¡c luªt k¸t hñp d¤ng xu §t b i to¡n ph¡t hi»n luªt sporadic khaeng tuy»t èi hai ng÷ïng mí v giîi thi»u thuªt to¡n MFISI nh¬m t¼m c¡c tªp möc dú li»u sporadic (tªp sporadic) khaeng tuy»t èi hai ng÷ïng mí cho c¡c luªt n y. Thuªt to¡n MFISI ÷ñc ph¡t triºn düa tr ¶n þ t÷ðng cõa thuªt to¡n MCISI t¼m tªp sporadic khaeng tuy»t èi hai ng÷ïng tø CSDL t¡c vö.…”

Section: Caeng Trnh Lin Quanunclassified

Mining fuzzy imperfectly sporadic rules with two threshold

Thủy¹,

Thuỵ²

2012

JCC

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tin Kinh t¸, Håc vi»n T i ch½nh 2 Khoa Caeng ngh» Thaeng tin, ¤i håc Caeng ngh», ¤i håc Quèc gia H Nëi Tóm tắt. Luªt sporadic l luªt k¸t hñp hi¸m. Luªt sporadic ÷ñc chia th nh hai lo¤i l : luªt sporadic tuy»t èi v luªt sporadic khaeng tuy»t èi. V §n • ph¡t hi»n luªt sporadic tuy»t èi v• cì b£n ¢ ÷ñc gi£i quy¸t tr ¶n c£ cì sð dú li»u (CSDL) t¡c vö v ành l÷ñng. Tuy nhi ¶n, cho ¸n nay ng÷íi ta mîi nghi ¶n cùu • xu §t mët sè ph÷ìng ph¡p t¼m c¡c luªt sporadic khaeng tuy»t èi tr ¶n CSDL t¡c vö. V §n • ph¡t hi»n c¡c luªt sporadic khaeng tuy»t èi tr ¶n CSDL ành l÷ñng v¨n ch÷a ÷ñc nghi ¶n cùu • xu §t. B i b¡o n y s³ gi£i quy¸t mët ph¦n v §n • â. Giîi thi»u thuªt to¡n MFISI (Mining Fuzzy Imperfectly Sporadic Itemsets) düa tr ¶n thuªt to¡n MCISI º t¼m c¡c tªp möc dú li»u sporadic khaeng tuy»t èi hai ng÷ïng mí. Mët sè k¸t qu£ thüc nghi»m t¼m tªp möc dú li»u sporadic khaeng tuy»t èi hai ng÷ïng mí tr ¶n cì sð dú li»u thüc Census Income ¢ ÷ñc ch¿ ra.

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Mining interesting imperfectly sporadic rules

Cited by 24 publications

References 16 publications

A Markov logic network method for reconstructing association rule-mining tasks in library book recommendation

A Markov logic network method for reconstructing association rule-mining tasks in library book recommendation

Outlier detection in relational data: A case study in geographical information systems

Mining fuzzy imperfectly sporadic rules with two threshold

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