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Efficient and Versatile FPGA Acceleration of Support Counting for Stream Mining of Sequences and Frequent Itemsets

Adrien Prost-Boucle, FRédéric Pétrot, Vincent Leroy, Hande Alemdar
2017 ACM Transactions on Reconfigurable Technology and Systems  
This accelerator can be used to accelerate data mining algorithms including itemsets and sequences mining.  ...  Streams of data are produced continuously, and are mined to extract patterns characterizing the data.  ...  Our hardware accelerator loads the set of candidate patterns and maintains a count of their frequency over a stream of data.  ... 
doi:10.1145/3027485 fatcat:ujsomdbfbrfbhipe5ih7emxiha

Sequential pattern mining with the Micron automata processor

Ke Wang, Elaheh Sadredini, Kevin Skadron
2016 Proceedings of the ACM International Conference on Computing Frontiers - CF '16  
When compared with the simple set mining problem and string mining problem, the hierarchical structure of sequential pattern mining (due to the need to consider frequent subsets within each itemset, as  ...  Sequential pattern mining (SPM) is a widely used data mining technique for discovering common sequences of events in large databases.  ...  Increasing throughput per node via hardware acceleration is desirable for throughput as well as energy efficiency, but even though hardware accelerators have been widely used in frequent set mining and  ... 
doi:10.1145/2903150.2903172 dblp:conf/cd/WangSS16 fatcat:kt4xf7sk5zbzphahbjnkph4cyu

Accelerating Itemset Sampling using Satisfiability Constraints on FPGA

Mael Gueguen, Olivier Sentieys, Alexandre Termier
2019 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE)  
Finding recurrent patterns within a data stream is important for fields as diverse as cybersecurity or e-commerce. This requires to use pattern mining techniques.  ...  We show that our accelerator can outperform a state-of-the-art implementation on a server class CPU using a modest FPGA product.  ...  This paves the way for future pattern sampling hardware accelerators, able to extract on the fly patterns from data streams with a very high throughput.  ... 
doi:10.23919/date.2019.8714932 dblp:conf/date/GueguenST19 fatcat:z65cdjif4jarjjyyxbvbqcnkry

FPGA-Based Stream Processing for Frequent Itemset Mining with Incremental Multiple Hashes

Kasho Yamamoto, Masayuki Ikebe, Tetsuya Asai, Masato Motomura
2016 Circuits and Systems  
In this study, we focused on identifying a promising stream mining algorithm, specifically a Frequent Itemset Mining (FIsM) algorithm, then we improved its performance using an FPGA.  ...  As a result, stream mining, extracting useful knowledge from a huge amount of data in real time, is attracting more and more attention.  ...  Therefore, algorithms that find frequent itemsets in stream data in real time, i.e., stream mining, have been studied extensively.  ... 
doi:10.4236/cs.2016.710281 fatcat:udgheanqf5bqjgw76izyysrsdm

Association Rule Mining with the Micron Automata Processor

Ke Wang, Yanjun Qi, Jeffrey J. Fox, Mircea R. Stan, Kevin Skadron
2015 2015 IEEE International Parallel and Distributed Processing Symposium  
Association rule mining (ARM) is a widely used data mining technique for discovering sets of frequently associated items in large databases.  ...  We accelerate ARM by using Micron's Automata Processor (AP), a hardware implementation of non-deterministic finite automata (NFAs), with additional features that significantly expand the APs capabilities  ...  MF14S-021-IT; by C-FAR, one of the six SRC STARnet Centers, sponsored by MARCO and DARPA; NSF grant EF-1124931; and a grant from Micron Technology.  ... 
doi:10.1109/ipdps.2015.101 dblp:conf/ipps/WangQFSS15 fatcat:cpxy5oijozgkhlazyl4zorspnq

Accelerating Parallel Frequent Itemset Mining on Graphics Processors with Sorting [chapter]

Yuan-Shao Huang, Kun-Ming Yu, Li-Wei Zhou, Ching-Hsien Hsu, Sheng-Hui Liu
2013 Lecture Notes in Computer Science  
Frequent Itemset Mining (FIM) is one of the main tasks in data mining field which aims at finding interesting patterns from databases.  ...  In this paper, an Accelerating Parallel Frequent Itemset Mining on Graphics Processors with Sorting (APFMS) algorithm is presented.  ... 
doi:10.1007/978-3-642-40820-5_21 fatcat:uxkws7j5ofdyhiwsxxej4iijeq

Frequent itemset mining on graphics processors

Wenbin Fang, Mian Lu, Xiangye Xiao, Bingsheng He, Qiong Luo
2009 Proceedings of the Fifth International Workshop on Data Management on New Hardware - DaMoN '09  
We present two efficient Apriori implementations of Frequent Itemset Mining (FIM) that utilize new-generation graphics processing units (GPUs).  ...  One implementation runs entirely on the GPU and eliminates intermediate data transfer between the GPU memory and the CPU memory.  ...  Frequent Itemset Mining The Frequent Itemset Mining (FIM) problem was introduced by Agrawal et al. [2] , as the first step to mine association rules in market basket data.  ... 
doi:10.1145/1565694.1565702 dblp:conf/damon/FangLXHL09 fatcat:ur7z4tb3njbj5drgktt3dqdewm

Eclat Algorithm for FIM on CPU-GPU co-operative & parallel environment

Sarika S Kadam, Sudarshan S.Deshmukh
2014 IOSR Journal of Computer Engineering  
Extracting the frequent itemsets from a transactional database is a fundamental task in data mining field because of its broad applications in mining association rules, time series, correlations etc.  ...  Thus, the aim of our approach is to develop efficient parallel new advanced Eclat strategy of Frequent Itemset Mining that utilize new-generation graphics processing units (GPUs) to speed-up the process  ...  The Apriorialgorithm [4] is not only applied in frequent itemset mining or association mining, but also in other data mining tasks, such as clustering , and functional dependency .  ... 
doi:10.9790/0661-16288896 fatcat:nfpnpg3s4rcntfsryuo5t2lie4

Enhancing FP-Growth Performance Using Multi-threading based on Comparative Study

Yousef K. Abu Samra, Ashraf Y. A. Maghari
2015 International Journal of Intelligent Computing Research  
Association rule mining has been focused as a major challenge within the field of data mining in research for over a decade.  ...  used in finding frequent itemsets in the transaction database.  ...  By this we can accelerate the process of mining frequent patterns multiple times regardless of the hardware it runs on.  ... 
doi:10.20533/ijicr.2042.4655.2015.0076 fatcat:qow7zf7bpbazxema7jr6aajvcu

A Survey of Parallel Sequential Pattern Mining [article]

Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, Philip S. Yu
2019 arXiv   pre-print
Some advanced topics for PSPM, including parallel quantitative / weighted / utility sequential pattern mining, PSPM from uncertain data and stream data, hardware acceleration for PSPM, are further reviewed  ...  However, it is more complex and challenging than other pattern mining tasks, i.e., frequent itemset mining and association rule mining, and also suffers from the above challenges when handling the large-scale  ...  This research was partially supported by the National Natural Science Foundation of China (NSFC) under Grant No.61503092, and by the China Scholarship Council Program.  ... 
arXiv:1805.10515v2 fatcat:6bothuniprd7xclmpwx26s6udu

Frequent Pattern-growth Algorithm on Multi-core CPU and GPU Processors

Khedija Arour, Amani Belkahla
2014 Journal of Computing and Information Technology  
Discovering association rules that identify relationships among sets of items is an important problem in data mining.  ...  It's a two steps process, the first step finds all frequent itemsets and the second one constructs association rules from these frequent sets.  ...  Introduction Frequent Itemset Mining (FIM) also known as Frequent Pattern Mining is one of the most popular problems in data mining, which consists of discovering frequently co-occurred itemsets and then  ... 
doi:10.2498/cit.1002361 fatcat:nqj7jpc77naa5oebtjtetjtd6a

A Reconfigurable Platform for Frequent Pattern Mining

Song Sun, Michael Steffen, Joseph Zambreno
2008 2008 International Conference on Reconfigurable Computing and FPGAs  
In this paper, a new hardware architecture for frequent pattern mining based on a systolic tree structure is proposed.  ...  The goal of this architecture is to mimic the internal memory layout of the original FP-growth algorithm while achieving a much higher throughput.  ...  Section 2 describes related work in the area of data mining acceleration.  ... 
doi:10.1109/reconfig.2008.80 dblp:conf/reconfig/SunSZ08 fatcat:xu7rk55ez5hi5jiy2fowkf4hli

Evaluation of Frequent Itemset Mining Platforms using Apriori and FP-Growth Algorithm [article]

Ravi Ranjan, Aditi Sharma
2019 arXiv   pre-print
Companies are recognizing that big data can be used to make more accurate predictions, and can be used to enhance the business with the help of appropriate association rule mining algorithm.  ...  two widely used algorithms Apriori and Fp-Growth on different scales of dataset.  ...  Master should be deployed on good configuration hardware.  ... 
arXiv:1902.10999v1 fatcat:jevdnzippfesrgz5asbputaauq

Algorithms for Extracting Frequent Episodes in the Process of Temporal Data Mining

Alexandru PIRJAN
2010 Informatică economică  
The purpose of this paper is to study the frequent episodes data mining through the use of parallel pattern discovery algorithms.  ...  An important aspect in the data mining process is the discovery of patterns having a great influence on the studied problem.  ...  This is useful for mining frequent itemsets on shared memory machines.  ... 
doaj:b4f9f47e312f4ac98df8108c47a94d9d fatcat:3xkkt7d7lbcezf7rggxwz5o6fa

Automata Processor Architecture and Applications: A Survey

Nourah A. Almubarak, Anwar Alshammeri, Imtiaz Ahmad
2016 International Journal of Grid and Distributed Computing  
complex data streams simultaneously to accelerate solving massively complex problems.  ...  In this paper, we present a survey of the state-of-the-art in automata processor based hardware accelerators.  ...  Association Rule Mining Association rule mining (ARM) [27] is a widely used data mining technique for discovering sets of frequently associated items in large databases.  ... 
doi:10.14257/ijgdc.2016.9.4.05 fatcat:stay6tyhrzgdhfn4ptnv56oxq4
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