Finding Minimal Rare Itemsets and Rare Association Rules

Background: Knowledge discovering rare itemsets mining, association Rules are significant in a transactional dataset. Be that as it may, rare association rules are now and again more intriguing than frequent association rules since rare rules indicate an unforeseen or obscure association. The utilization of rare itemset mining with item selector is unavoidable and, in this way, has turned into a rising field of research, this subject has numerous difficulties. Objective: To find the revenue examination of marketing sector by rare itemsets selector by threshold and time series-based prediction technique. Methods: This paper gives the revenue examination of marketing sector by rare itemsets selector by threshold and time series-based prediction technique. A new algorithm for locating the rare itemsets by Association Rare itemset Rule Mining (ARIRM) to produced rules then utility itemsets discover by the threshold. When the rare patterns are analyzed, the ARIMA model used to anticipate the revenue. Based on the investigation of rare showcasing data with rules of the mining space, this methodology uses a tree structure to learn the rare items. Results: The test results the 'K' transactions with high revenues discovered utilizing the proposed model contrasted with other existing procedures; this forecast procedure is helpful for upcoming transactions. Conclusion: Based on the investigation of rare showcasing data with rules of the mining space, this methodology uses a tree structure to learn the rare items.

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Improved approaches to mine rare association rules in transactional databases

Proceedings of the Fourth SIGMOD PhD Workshop on Innovative Database Research - IDAR '10 ◽

10.1145/1811136.1811140 ◽

2010 ◽

Cited By ~ 2

Author(s):

R. Uday Kiran ◽

P. Krishna Reddy

Keyword(s):

Association Rules ◽

Rare Association ◽

Transactional Databases

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An Efficient Method for Mining Rare Association Rules: A Case Study on Air Pollution

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213021500184 ◽

2021 ◽

Vol 30 (04) ◽

pp. 2150018

Author(s):

Anindita Borah ◽

Bhabesh Nath

Keyword(s):

Air Pollution ◽

Association Rules ◽

Air Pollutants ◽

Pattern Mining ◽

Environmental Parameters ◽

Pollution Monitoring ◽

Air Pollution Monitoring ◽

Rare Association ◽

Support Threshold ◽

Complete Set

Most pattern mining techniques almost singularly focus on identifying frequent patterns and very less attention has been paid to the generation of rare patterns. However, in several domains, recognizing less frequent but strongly related patterns have greater advantage over the former ones. Identification of compelling and meaningful rare associations among such patterns may proved to be significant for air quality management that has become an indispensable task in today’s world. The rare correlations between air pollutants and other parameters may aid in restricting the air pollution to a manageable level. To this end, efficient and competent rare pattern mining techniques are needed that can generate the complete set of rare patterns, further identifying significant rare association rules among them. Moreover, a notable issue with databases is their continuous update over time due to the addition of new records. The users requirement or behavior may change with the incremental update of databases that makes it difficult to determine a suitable support threshold for the extraction of interesting rare association rules. This paper, presents an efficient rare pattern mining technique to capture the complete set of rare patterns from a real environmental dataset. The proposed approach does not restart the entire mining process upon threshold update and generates the complete set of rare association rules in a single database scan. It can effectively perform incremental mining and also provides flexibility to the user to regulate the value of support threshold for generating the rare patterns. Significant rare association rules representing correlations between air pollutants and other environmental parameters are further extracted from the generated rare patterns to identify the substantial causes of air pollution. Performance analysis shows that the proposed method is more efficient than existing rare pattern mining approaches in providing significant directions to the domain experts for air pollution monitoring.

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