Reliable frequent itemsets mining with actor-based Apriori algorithm

Marek Puścian

doi:10.1117/12.2536934

6 November 2019 Reliable frequent itemsets mining with actor-based Apriori algorithm

Marek Puścian

Proceedings Volume 11176, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019; 1117621 (2019) https://doi.org/10.1117/12.2536934
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, 2019, Wilga, Poland

Abstract

This paper presents an actor-based Apriori algorithm enhanced with fault tolerance mechanism. All phases of the algorithm including candidate generation and support counting operations are performed by asynchronous actors. When an error occurs during the execution of the algorithm, calculations are interrupted locally for specific actors. The actor state is restored from the snapshot and the operations that caused the failure are either repeated or skipped. Other actors progress with their current tasks. The algorithm can be executed in parallel and distributed environments. Proposed enhancements have been successfully implemented using JAVA and Akka library. This paper discusses the results of the performance of actor-based Apriori algorithm against different datasets. The presented approach has been illustrated with many experiments and measurements performed using multiprocessor and multithreaded computer.

Citation Download Citation

Marek Puścian "Reliable frequent itemsets mining with actor-based Apriori algorithm", Proc. SPIE 11176, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, 1117621 (6 November 2019); https://doi.org/10.1117/12.2536934

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