Frequent itemset minning with trie data structure and parallel execution with PVM

Date

2007-10-03

Author

Guner, Levent
Karagöz, Pınar

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

156
views

0
downloads

Apriori algorithm is one of the basic algorithms introduced to solve the problem of frequent itemset mining (FIM). Since there is a new generation of affordable computers with parallel processing capability and it is easier to set up computer clusters, we can develop more efficient parallel FIM algorithms for these new systems. This paper investigates the use of trie data structure in parallel execution of Apriori algorithm, the potential problems during implementation, performance comparison of several parallel implementations and in order to increase the efficiency, proposes a new way of message passing for parallel Apriori on a computer cluster with PVM.

Subject Keywords

Message passing, Trie, Parallel execution, PVM, Apriori

URI

https://hdl.handle.net/11511/62444

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Hierarchical Parallelization of the Multilevel Fast Multipole Algorithm (MLFMA) Gurel, Levent; Ergül, Özgür Salih (2013-02-01) Due to its O(NlogN) complexity, the multilevel fast multipole algorithm (MLFMA) is one of the most prized algorithms of computational electromagnetics and certain other disciplines. Various implementations of this algorithm have been used for rigorous solutions of large-scale scattering, radiation, and miscellaneous other electromagnetics problems involving 3-D objects with arbitrary geometries. Parallelization of MLFMA is crucial for solving real-life problems discretized with hundreds of millions of unkno...
MODELLING OF KERNEL MACHINES BY INFINITE AND SEMI-INFINITE PROGRAMMING Ozogur-Akyuz, S.; Weber, Gerhard Wilhelm (2009-06-03) In Machine Learning (ML) algorithms, one of the crucial issues is the representation of the data. As the data become heterogeneous and large-scale, single kernel methods become insufficient to classify nonlinear data. The finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, we propose a novel method of "infinite" kernel combinations for learning problems with the help of infinite and semi-infinite programming regarding all elements in kernel space. Looking...
Asymptotically optimal importance sampling for Jackson networks with a tree topology Sezer, Ali Devin (2010-02-01) This note describes an importance sampling (IS) algorithm to estimate buffer overflows of stable Jackson networks with a tree topology. Three new measures of service capacity and traffic in Jackson networks are introduced and the algorithm is defined in their terms. These measures are effective service rate, effective utilization and effective service-to-arrival ratio of a node. They depend on the nonempty/empty states of the queues of the network. For a node with a nonempty queue, the effective service rat...
Benchmark Solutions of Large Problems for Evaluating Accuracy and Efficiency of Electromagnetics Solvers Gurel, Levent; Ergül, Özgür Salih (2011-07-08) We present a set of benchmark problems involving conducting spheres and their solutions using a parallel implementation of the multilevel fast multipole algorithm (MLFMA). Accuracy of the implementation is tested by comparing the computational results with analytical Mie-series solutions. Reference solutions are made available on an interactive website to evaluate and compare the accuracy and efficiency of fast solvers. We also demonstrate the capabilities of our solver on real-life problems involving compl...
Efficient parallelization of multilevel fast multipole algorithm Ergül, Özgür Salih (null; 2006-11-10) We report our efforts for the solution of large electromagnetics problems accurately and efficiently with the parallel multilevel fast multipole algorithm. We carefully investigate different stages of the parallelization and identify the bottlenecks to develop new strategies. The required modifications are implemented in order to increase the efficiency of the solutions of scattering problems involving various geometries.

Citation Formats

L. Guner and P. Karagöz, “Frequent itemset minning with trie data structure and parallel execution with PVM,” 2007, vol. 4757, p. 289, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62444.