Probabilistic Sequence Mining – Evaluation and Extension of ProMFS Algorithm for Real-Time Problems

Hryniów, Krzysztof; Dzieliński, Andrzej

doi:10.2478/v10177-012-0044-0

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…FP-growth algorithm avoids constant database scans by creating the tree in the first step and then mining smaller search-space using divide-and-conquer methods. Apriori GSP SPARSE SPADE SPAM LAPIN King James 1% 69994 5418 5645 5214 6781 5514 Bible 0,03% 86112 6519 6982 6706 9400 6618 Pan Tadeusz 2% 2804 422 437 401 363 428 0,02% 4736 647 652 647 580 619 Connect 1% 64658 4519 3850 4421 4536 4028 Chess 0,4% 182 29 23 25 31 27 Pumsb* 0,5% 147208 8143 6415 6999 8548 6330 Sequential pattern mining algorithms are proven to work well and with satisfying speed for different types of data sets [6]- [8], but for large data sets tend to be too slow to be used for real-time solutions [9], [10]. Tests of popular CPU-based algorithms performed on i7 CPU 960@3.2GHz with 32GB RAM under MS VS 2010 were made.…”

Section: Introductionmentioning

confidence: 99%

Parallel pattern mining on Graphics Processing Units

Hryniów

2013

Proceedings of the 14th International Carpathian Control Conference (ICCC)

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Frequent pattern mining is a field with many practical applications, where large computational power and speed are needed. Many state-of-the-art frequent pattern mining applications are an inefficient solutions for both shared memory and multiprocessor systems due to problems with parallelism and memory. One of possible solutions to the problem is the use of Graphics Processing Unit (GPU) in the system along with modification of classical pattern mining algorithms in such a way, that the sequential part of algorithm is run on host and the parallel part on GPU. Such solution allows for considerable speed-up (of up to two orders of magnitude), but for more complicated problems and FPM algorithms it can be hard to achieve.So far there were presented 3 modifications of the most basic Apriori algorithm for solving GPGPU (general-purpose computation on graphics hardware) problems. Each of proposed parallel implementations (PBI, TBI, GPA) is suited only for frequent itemset mining, furthermore even the best of them (TBI) obtains results only 2-5 times faster then CPU-based versions of FP-growth algorithm. On ICCC'12 there was presented parallel version of more complex GSP algorithm adjusted for GPGPU problems, capable of obtaining results 50-100 times faster then CPU-based version, with the same accuracy. Not only is the GPGPU implementation the fastest, but also it is suited for solving more complex frequent pattern mining problems.This paper presents extension of proposed modifications to more complex algorithms from Apriori family. The modifications are evaluated both theoretically and with use of experimental setup consisting of nVidia Tesla card and CUDA parallel computing platform. Solution proposed in the paper uses more complex, faster frequent pattern mining algorithms then GSP, making it well suited for solving real-time GPGPU problems for very large data sets.Index Terms-CUDA, GPGPU, GSP, sequential pattern mining, SPARSE.

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Section: Introductionmentioning

confidence: 99%

Parallel pattern mining on Graphics Processing Units

Hryniów

2013

Proceedings of the 14th International Carpathian Control Conference (ICCC)

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“…GSP algorithm is proven to work well and with satisfying speed for different types of data sets [3]- [5], but as shown in Table I for large data sets the classical GSP algorithm is too slow to be used for real-time solutions. Analogous case is for other sequential pattern mining algorithms [6].…”

Section: Introductionmentioning

confidence: 99%

Parallel pattern mining - application of GSP algorithm for Graphics Processing Units

Hryniów

2012

Proceedings of the 13th International Carpathian Control Conference (ICCC)

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Frequent pattern mining is the field with many practical applications, where large computational power and speed are needed. Many solutions, both software and hardware, are proposed for those applications, but specialised solutions in form of embedded systems are not so common as one could imagine. This is especially true when we consider problems that can be paralleled. Many of the state-of-the-art frequent pattern mining applications are inefficient when used on shared memory systems or multiprocessor systems. To solve this problem both hardware and software solutions are proposed -remapping system architecture, improving memory performance, modifying task allocation.This article proposes modification of classical frequent pattern mining algorithms from Apriori family, illustrated with the example of popular GSP algorithm. Addition of GPU (Graphics Processing Unit) or multiple GPUs to embedded system is proposed and algorithm is modified in such a way, that it is best suited for solving GPGPU (general-purpose computation on graphics hardware) problems. Both theoretical and experimental evaluation of modifications are made, the latter with use of setup consisting of NVIDIA Tesla card and CUDA parallel computing platform.It is shown in the article, that for tested data sets modified GSP algorithm finishes finding frequent sequences 50-100 times faster and with the same accuracy. Such speed-up allows the use of classical pattern mining algorithms for real-time solutions. This also permits broader scope of algorithms to be used in embedded systems with real-time constraints.Index Terms-embedded systems, sequential pattern mining, CUDA, GPGPU, GSP.

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Probabilistic Sequence Mining – Evaluation and Extension of ProMFS Algorithm for Real-Time Problems

Cited by 2 publications

References 11 publications

Parallel pattern mining on Graphics Processing Units

Parallel pattern mining on Graphics Processing Units

Parallel pattern mining - application of GSP algorithm for Graphics Processing Units

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