Publication Type : Conference Paper
Publisher : Proceedings of the 2016 International Conference on Data Science and Engineering,
Source : Proceedings of the 2016 International Conference on Data Science and Engineering, ICDSE 2016, Institute of Electrical and Electronics Engineers Inc.
ISBN : 9781509012800
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
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Center : AI (Artificial Intelligence) and Distributed Systems
Department : Computer Science
Verified : No
Year : 2016
Abstract : 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.
Cite this Research Publication : Sandhya 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