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.
This paper presents a sequential frequent itemsets mining algorithm Apriori that is adapted to concurrent processing. It
applies Master Slave scheme to candidate generation and support counting operations performed by threads on a single
machine. Two approaches to traversing shared prefix tree and counting support of itemsets are presented and compared.
Several optimization methods have been proposed for the multithreaded environment. Proposed enhancements have been
successfully implemented using JAVA. This paper discusses results of the performance of concurrent Apriori algorithm
against different datasets. Presented approach has been illustrated with many experiments and measurements performed
using multiprocessor and multithreaded computer.
This paper presents a frequent item mining algorithm that was customized to handle growing data repositories. The proposed solution applies Master Slave scheme to frequent pattern growth technique. Efficient utilization of available computation units is achieved by dynamic reallocation of tasks. Conditional frequent trees are assigned to parallel workers basing on their workload. Proposed enhancements have been successfully implemented using Charm++ library. This paper discusses results of the performance of parallelized FP-growth algorithm against different datasets. The approach has been illustrated with many experiments and measurements performed using multiprocessor and multithreaded computer.
In this paper we describe Eclat algorithm that is adapted to deal with growing data repositories. The presented solution utilizes Master-Slave scheme to distribute data mining tasks among available computation nodes. Several improvements have been proposed and successfully implemented using Charm++ library. This paper introduces optimization techniques to reduce communication cost and synchronization overhead. It also discusses results of the performance of parallel Eclat algorithm against different databases and compares it with parallel Apriori algorithm. The proposed approach has been illustrated with many experiments and measurements performed using multiprocessor and multithreaded computer platform.
KEYWORDS: Mining, Parallel processing, Optimization (mathematics), Data mining, Databases, Data storage, Data communications, Computing systems, Information technology, Photonics
This paper deals with the problem of adapting sequential frequent item sets mining algorithm to parallel processing. The original Bodon's Apriori algorithm has been partitioned into loosely coupled tasks and prepared to be executed on several computation nodes using Charm++ library. Variety of optimization methods have been proposed and successfully implemented in parallel environment. The work provides enhancements to achieve good efficiency during parallelization of existing solutions, e.g.: how to organize communication between tasks. The presented approach has been illustrated with many experiments and measurements performed on parallelized algorithm.
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