FARM: hierarchical association rule mining and visualization method

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FARM: hierarchical association rule mining and visualization method

Petr T., Oleg S., Lukashina N., Daria L., Maxim A.

Associations search is one of the methods of data analysis. Association Rule Mining (ARM) approach can construct association rules from observational data, but the most widely used algorithm Apriori typically produces large number of unstructured results without any ranking or statistical significance. We propose a novel method for association rules mining FARM (Fishbone Association Rule Mining) to address these challenges.

Language: English

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Keywords: association rule mining

In book

Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

NY: Association for Computing Machinery (ACM), 2021.

Approximate Computation of Exact Association Rules

Bansal S., Kailasam S., Obiedkov S., , in: Formal Concept Analysis: 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 – July 2, 2021, Proceedings.: Springer, 2021. P. 107–122.

We adapt a polynomial-time approximation algorithm for computing the canonical basis of implications to approximately compute frequent implications, also known as exact association rules. To this end, we define a suitable notion of approximation that takes into account the frequency of attribute subsets and show that our algorithm achieves a desired approximation with high probability. ...

Added: December 9, 2021

Mining Definitions from RDF Annotations Using Formal Concept Analysis

Alam M., Buzmakov A. V., Codocedo V. et al., , in: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015.: Palo Alto: AAAI Press, 2015. P. 823–829.

The popularization and quick growth of Linked Open Data (LOD) has led to challenging aspects regarding quality assessment and data exploration of the RDF triples that shape the LOD cloud. Particularly, we are interested in the completeness of data and its potential to provide concept definitions in terms of necessary and sufficient conditions. In this ...

Added: October 22, 2015

Machine Learning and Data Mining in Pattern Recognition

Springer, 2014.

This book constitutes the refereed proceedings of the 10th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2014, held in St. Petersburg, Russia in July 2014. The 40 full papers presented were carefully reviewed and selected from 128 submissions. The topics range from theoretical topics for classification, clustering, association rule and ...

Added: September 30, 2014