Publication Type:

Conference Paper

Source:

Proceedings of the 2016 International Conference on Data Science and Engineering, ICDSE 2016, Institute of Electrical and Electronics Engineers Inc. (2016)

ISBN:

9781509012800

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015179445&doi=10.1109%2fICDSE.2016.7823952&partnerID=40&md5=19c371582527e6546c2bde50a1c7cd2c

Keywords:

Apriori, Apriori algorithms, Association rules, Clustering algorithms, Data mining, decision making, Digital storage, High dimensional data, High dimensional datasets, Learning algorithms, Q R decomposition, Storage and retrievals

Abstract:

<p>Apriori is one of the best algorithms for learning association rules. Due to the explosion of data, the storage and retrieval mechanisms in various database paradigms have revolutionized the technologies and methodologies used in the architecture. As a result, the database is not only utilized for mere information retrieval but also to infer the analytical aspect of data. Therefore it is essential to find association rules from high dimensional data because the correlation amongst the attributes can help in gaining deeper insight into the data and help in decision making, recommendations as well as reorganizing the data for effective retrieval. The traditional Apriori algorithm is computationally expensive and infeasible with high dimensional datasets. Hence we propose a variant of Apriori algorithm using the concept of QR decomposition for reducing the dimensions thereby reducing the complexity of the traditional Apriori algorithm. © 2016 IEEE.</p>

Notes:

cited By 0; Conference of 2016 3rd International Conference on Data Science and Engineering, ICDSE 2016 ; Conference Date: 23 August 2016 Through 25 August 2016; Conference Code:126030

Cite this Research Publication

S.a Harikumar and Dilipkumar, D. U., “Apriori algorithm for association rule mining in high dimensional data”, in Proceedings of the 2016 International Conference on Data Science and Engineering, ICDSE 2016, 2016.

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