Mining for strong negative associations in a large database of customer transactions

Savasere, Ashoka; Omiecinski, Edward; Navathe, Shamkant B.

doi:10.1109/icde.1998.655812

Cited by 196 publications

(154 citation statements)

References 5 publications

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“…Negative association was first pointed out by Brin et al in [6]. Since then, many techniques for mining negative associations have been developed [10,14,18]. In the case of negative associations we are interested in finding itemsets that have a very low probability of occurring together.…”

Section: A Negative Association Rulesmentioning

confidence: 99%

“…However, their sole purpose of using probe attributes is to perform attribute clustering. Indirect association is closely related to the notion of negative association rules [27]. In both cases, we are dealing with itemsets that do not have sufficiently high support.…”

Section: Related Work In Indirect Association Rule Miningmentioning

confidence: 99%

“…Mining negative association rules is computational intractable with a naive approach because billions of negative associations may be found in a large database while almost all of them are extremely uninteresting. This problem was addressed in [10] by combining previously discovered positive associations with domain knowledge to constrain the search space such that fewer but more interesting negative rules are mined. A general framework for mining both positive and negative association rules of interest was presented in [18], in which no domain knowledge was required and the negative association rules were given in more concrete expressions to indicate actual relationships between different itemsets.…”

Section: A Negative Association Rulesmentioning

confidence: 99%

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Mining Indirect Positive and Negative Association Rules: A Novel Approach

eddy¹

2014

IJIRCCE

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Indirect association is a new kind of infrequent pattern, which provides a new way for interpreting the value of infrequent patterns and can effectively reduce the number of uninteresting infrequent patterns. The concept of indirect association is to "indirectly" connect two rarely co-occurred items via a frequent itemset called mediator, and if appropriately utilized it can help to identify real interesting "infrequent itempairs" from databases. Indirect association rule is said to be positive (Negative) if mediator set contains presence (presence or absence) of items. Existing indirect association mining methods mine positive mediator sets. To the best of our knowledge, no research work has been conducted on mining indirect negative associations. In this paper, we propose an approach for mining indirect negative associations. The proposed method can discover all positive and negative indirect association between itemsets.

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Section: A Negative Association Rulesmentioning

confidence: 99%

Section: Related Work In Indirect Association Rule Miningmentioning

confidence: 99%

Section: A Negative Association Rulesmentioning

confidence: 99%

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Mining Indirect Positive and Negative Association Rules: A Novel Approach

eddy¹

2014

IJIRCCE

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“…The fuzzy ARM algorithm [4] transforms quantitative value into a fuzzy set with the linguistic terms by using the membership functions. The scalar count of each linguistic term is estimated.…”

Section: Algorithm For Mining Positive and Negative Weighted Fuzzymentioning

confidence: 99%

F-PNWAR: Fuzzy-based Positive and Negative Weighted Association Rule Mining Algorithm

Mangayarkkarasi¹,

Chidambaram²

2017

IJET

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“…end if (11) end for (12) // Pruning using Apriori property (13) for each (k-1)-subsets s of c in C k (14) If s is not a member of L k-1 (15) C k =C k -{c} (16) end if (17) end for (18) PC k = C k; (19) for each c in PC K (20) NC The generation of positive rules continues without disruption and the rich but valuable negative rules are produced as by-products of the Apriori process.…”

Section: Inputmentioning

confidence: 99%

Mining positive and negative association rules

Ramasubbareddy¹,

Govardhan

Ramamohanreddy

2010

2010 5th International Conference on Computer Science &Amp; Education

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Association rule mining is one of the most popular data mining techniques to find associations among items in a set by mining necessary patterns in a large database. Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items (i.e. absent from transactions). Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. They are also very useful for constructing associative classifiers. In this paper, we propose an algorithm that mines positive and negative association rules without adding any additional measure and extra database scans.

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Mining for strong negative associations in a large database of customer transactions

Cited by 196 publications

References 5 publications

Mining Indirect Positive and Negative Association Rules: A Novel Approach

Mining Indirect Positive and Negative Association Rules: A Novel Approach

F-PNWAR: Fuzzy-based Positive and Negative Weighted Association Rule Mining Algorithm

Mining positive and negative association rules

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