Association Rule and Quantitative Association Rule Mining among Infrequent Items

Zhou, Ling; Yau, Shing‐Tung

doi:10.4018/978-1-60566-754-6.ch002

Cited by 11 publications

(18 citation statements)

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“…Different models are proposed in this framework. They are: (i) minimum constraint model [4,13,22,29] (ii) maximum constraint model [27] and (iii) other models [28,31].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…Those algorithms try to find all rare itemsets but they spend most of time for searching non-rare itemsets which tends to give us uninteresting association rules. To address the "rare item problem", "multiple minsup framework" [4,13,22,[27][28][29][30][31] is used to determine rare rules. Different models are proposed in this framework.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…Other Models This approach is proposed for mining rules by only considering items having support than the minsup which is infrequent items [28]. This approach fails to mine rule with rare and frequent items.…”

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confidence: 99%

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A Recent Overview: Rare Association Rule Mining

Bhatt¹,

Patel²

2014

IJCA

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Rare association rules are mine useful information form large dataset. Traditional association mining methods generate frequent rules based on frequent itemsets with reference of minimum support and minimum confidence threshold which specified by user. It called as support-confidence framework. As many of generated rules are of no use, further analysis is essential to find interesting Rules. A rule that contains rare items can consider as rare association rule. Rare Association Rules Represent unpredictable or unknown association, so it is more interesting than frequent association rule. Rare association rule mining provides relationship between items which occurs uncommonly. This paper presents brief survey in the area of rare association rule mining.

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“…Different models are proposed in this framework. They are: (i) minimum constraint model [4,13,22,29] (ii) maximum constraint model [27] and (iii) other models [28,31].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

See 1 more Smart Citation

A Recent Overview: Rare Association Rule Mining

Bhatt¹,

Patel²

2014

IJCA

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“…WSN missing data imputing methods are mainly based on statistics [2][3][4], association rules [5][6] and clustering [7][8][9] algorithm. Statistics method mainly obtains statistical data set through data analysis, and then uses this information to deal with missing values.…”

Section: Introductionmentioning

confidence: 99%

WSN Missing Data Imputing Based on Multiple Time Granularity

Liu

Zhang

et al. 2016

IJFGCN

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“…In the literature, efforts are being made to propose improved approaches to mine rare associations [10] [11] [12] [13]. In [10], instead of fixing single minsup value for all items, the minsup value is calculated for each item based on the percentage of its support and frequent itemsets are extracted if an itemset satisfied the lowest minsup value of the items in it.…”

Section: Introductionmentioning

confidence: 99%

An improved multiple minimum support based approach to mine rare association rules

Kiran

Re²

2009

2009 IEEE Symposium on Computational Intelligence and Data Mining

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Abstract-In this paper we have proposed an improved approach to extract rare association rules. Rare association rules are the association rules containing rare items. Rare items are less frequent items. For extracting rare itemsets, the single minimum support (minsup) based approaches like Apriori approach suffer from "rare item problem" dilemma. At high minsup value, rare itemsets are missed, and at low minsup value, the number of frequent itemsets explodes. To extract rare itemsets, an effort has been made in the literature in which minsup of each item is fixed equal to the percentage of its support. Even though this approach improves the performance over single minsup based approaches, it still suffers from "rare item problem" dilemma. If minsup for the item is fixed by setting the percentage value high, the rare itemsets are missed as the minsup for the rare items becomes close to their support, and if minsup for the item is fixed by setting the percentage value low, the number of frequent itemsets explodes. In this paper, we propose an improved approach in which minsup is fixed for each item based on the notion of "support difference". The proposed approach assigns appropriate minsup values for frequent as well as rare items based on their item supports and reduces both "rule missing" and "rule explosion" problems. Experimental results on both synthetic and real world datasets show that the proposed approach improves performance over existing approaches by minimizing the explosion of number of frequent itemsets involving frequent items and without missing the frequent itemsets involving rare items.

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Association Rule and Quantitative Association Rule Mining among Infrequent Items

Cited by 11 publications

References 0 publications

A Recent Overview: Rare Association Rule Mining

A Recent Overview: Rare Association Rule Mining

WSN Missing Data Imputing Based on Multiple Time Granularity

An improved multiple minimum support based approach to mine rare association rules

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