A Fast Algorithm for Mining Rare Itemsets

Troiano, Luigi; Scibelli, Giacomo; Birtolo, Cosimo

doi:10.1109/isda.2009.55

Cited by 58 publications

(27 citation statements)

References 10 publications

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“…Rarity [3], ARIMA [4], MS-Apriority [5], Apriority-Inverse [6] and Affirm [7] are five algorithms which discover rare item sets. All of them utilize level-wise algorithm similar to Apriority, which contains expensive candidate generation step and pruning step.…”

Section: Related Workmentioning

confidence: 99%

Discovery of Fuzzy Rare Association Rules from Large Transaction Databases

Ouyang¹

2017

Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)

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Abstract. Rare association rules is an association rule which has low support and high confidence. In recent years, the discovery of rare association rules has got quite a lot of attention, which has become a hot topic in data mining research. However, current discovery algorithms for rare association rules are built on the binary valued transaction databases, which cannot deal with quantitative attributes. In this paper, we put forward a discovery algorithm for finding fuzzy rare association rules to handle quantitative attributes. Experiments on the synthetic data stream show that the proposed algorithm is efficient and scalable.

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Section: Related Workmentioning

confidence: 99%

Discovery of Fuzzy Rare Association Rules from Large Transaction Databases

Ouyang¹

2017

Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)

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“…The reason for inefficiency of this algorithm was the removal of rules with higher MIS values. Troiano et al [7] analyzed the problem of bottom-up approach algorithms that searches through many levels. For reducing the number of searches the author proposed the Rarity algorithm that starts with identification of longest transaction from database and search rare itemsets in top-down approach.…”

Section: Related Workmentioning

confidence: 99%

Mining Interesting Rare Items with Maximum Constraint Model Based on Tree Structure

Bhatt

Patel

2015

2015 Fifth International Conference on Communication Systems and Network Technologies

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Rare association rule mining provides useful information from large database. Traditional association mining techniques generate frequent rules based on frequent itemsets with reference to user defined: minimum support threshold and minimum confidence threshold. It is known as support-confidence framework. As many of generated rules are of no use, further analysis is essential to find interesting Rules. Rare association rule contains Rare Items. Rare Association Rules represents unpredictable or unknown associations, so that it becomes more interesting than frequent association rule mining. The main goal of rare association rule mining is to discover relationships among set of items in a database that occurs uncommonly. We have proposed a Maximum Constraint based method for generating rare association rule with tree structure. Tentative results show that MCRP-Tree takes less time for rule generation compared to the existing algorithm as well as it finds more interesting rare items.

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“…• RARITY: RARITY algorithm [9] uses the same property as AfRARM [8]. It starts with the largest itemset and move downwards to the itemset of smaller length.…”

Section: Apriori Based Approachmentioning

confidence: 99%

Segregation of Rare Items Association

Rana

Mehta

Somkunwar

et al. 2015

Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics

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Nowadays there are many applications including rare itemsets. Here, this paper is concentrating Associations of rare itemsets as association rule mining is considered as one of the most important data mining techniques utilized in the area of market basket data analysis, stock data analysis for frequent items mining. Also it is applied for rare itemsets mining in applications like intrusion detection, medical science, etc. as they have special characteristic like appearing for less number of times. This paper is categorizing them according to the usages of different basic approach, storage structure, mining of items, number of database scans and threshold(s) used, proposing the approach to segregate the rare items from the study of the number of research works done in this area and analyzed the result.

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A Fast Algorithm for Mining Rare Itemsets

Cited by 58 publications

References 10 publications

Discovery of Fuzzy Rare Association Rules from Large Transaction Databases

Discovery of Fuzzy Rare Association Rules from Large Transaction Databases

Mining Interesting Rare Items with Maximum Constraint Model Based on Tree Structure

Segregation of Rare Items Association

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