scholarly journals Parallelization of Frequent Itemset Mining Methods with FP-tree: An Experiment with PrePost+ Algorithm

2021 ◽  
Vol 18 (2) ◽  
Keyword(s):  
Graphics Processing Units ◽  
General Purpose ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Complex Data ◽  
Itemset Mining ◽  
Mining Methods ◽  
Processing Algorithms ◽  
Graphics Processing ◽  
Data Processing Algorithms

Parallel processing has turn to be a common programming practice because of its efficiency and thus becomes an interesting field for researchers. With the introduction of multi- core processors as well as general purpose graphics processing units, parallel programming has become affordable. This leads to the parallelization of many of the complex data processing algorithms including algorithms in data mining. In this paper, a study on parallel PrePost+ is presented. PrePost+ is an efficient frequent itemset mining algorithm. The algorithm has been modified as a parallel algorithm and the obtained result is compared with the result of sequential PrePost+ algorithm

scholarly journals Monitor for Anti-Aircraft Guidance and Observation Systems

2019 ◽  
Vol 10 (2) ◽  
pp. 143-150
Author(s):  
Rafał KRUK ◽  
Zbigniew REMPAŁA
Keyword(s):  
Signal Processing ◽  
Graphics Processing Units ◽  
General Purpose ◽  
Performance Comparison ◽  
Digital Data ◽  
Data Filtering ◽  
Observation Systems ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Graphics Processing

The paper presents a discussion on the issue of possible acceleration of radiolocation signal processing algorithms in seekers using graphics processing units. A concept and implementation examples of algorithms performing digital data filtering on general purpose central and graphics processing units are introduced. The results of performance comparison of central and graphics processing units during computing discrete convolution are presented at the end of the paper.

scholarly journals Acceleration of Signal Processing Algorithms in Seekers Using Graphics Processing Units

2019 ◽  
Vol 10 (1) ◽  
pp. 91-98
Author(s):  
Piotr TUREK
Keyword(s):  
Signal Processing ◽  
Graphics Processing Units ◽  
General Purpose ◽  
Performance Comparison ◽  
Digital Data ◽  
Data Filtering ◽  
Discrete Convolution ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Graphics Processing

The paper presents a discussion on the issue of possible acceleration of radiolocation signal processing algorithms in seekers using graphics processing units. A concept and implementation examples of algorithms performing digital data filtering on general purpose central and graphics processing units are introduced. The results of performance comparison of central and graphics processing units during computing discrete convolution are presented at the end of the paper.

Mining Frequent Weighted Itemsets without Storing Transaction IDs and Generating Candidates

2017 ◽  
Vol 25 (01) ◽  
pp. 111-144 ◽  
Author(s):  
Gangin Lee ◽  
Unil Yun ◽  
Keun Ho Ryu
Keyword(s):  
State Of The Art ◽  
Frequent Itemset ◽  
Experimental Results ◽  
Frequent Itemset Mining ◽  
Memory Usage ◽  
Tree Structures ◽  
Prefix Tree ◽  
Itemset Mining ◽  
Mining Methods ◽  
Mining Algorithms

Weighted itemset mining, which is one of the important areas in frequent itemset mining, is an approach for mining meaningful itemsets considering different importance or weights for each item in databases. Because of the merit of the weighted itemset mining, various related works have been studied actively. As one of the methods in the weighted itemset mining, FWI (Frequent Weighted Itemset) mining calculates weights of transactions from weights of items and then finds FWIs based on the transaction weights. However, previous FWI mining methods still have limitations in terms of runtime and memory usage performance. For this reason, in this paper, we propose two algorithms for mining FWIs more efficiently from databases with weights of items. In contrast to the previous approaches storing transaction IDs for mining FWIs, the proposed methods employ new types of prefix tree structures and mine these patterns more efficiently without storing any transaction ID. Through extensive experimental results in this paper, we show that the proposed algorithms outperform state-of-the-art FWI mining algorithms in terms of runtime, memory usage, and scalability.

Efficient frequent itemset mining methods over time-sensitive streams

2014 ◽  
Vol 56 ◽  
pp. 281-298 ◽  
Author(s):  
Haifeng Li ◽  
Ning Zhang ◽  
Jianming Zhu ◽  
Huaihu Cao ◽  
Yue Wang
Keyword(s):  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Itemset Mining ◽  
Mining Methods ◽  
Over Time

Probabilistic maximal frequent itemset mining methods over uncertain databases

2019 ◽  
Vol 23 (6) ◽  
pp. 1219-1241
Author(s):  
Haifeng Li ◽  
Mo Hai ◽  
Ning Zhang ◽  
Jianming Zhu ◽  
Yue Wang ◽  
...  
Keyword(s):  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Uncertain Databases ◽  
Itemset Mining ◽  
Mining Methods

FPGA/GPU-based Acceleration for Frequent Itemsets Mining: A Comprehensive Review

2022 ◽  
Vol 54 (9) ◽  
pp. 1-35
Author(s):  
Lázaro Bustio-Martínez ◽  
René Cumplido ◽  
Martín Letras ◽  
Raudel Hernández-León ◽  
Claudia Feregrino-Uribe ◽  
...  
Keyword(s):  
Graphics Processing Units ◽  
Hardware Acceleration ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Comprehensive Review ◽  
Development Platform ◽  
Itemset Mining ◽  
Modern Development ◽  
Frequent Itemsets Mining

In data mining, Frequent Itemsets Mining is a technique used in several domains with notable results. However, the large volume of data in modern datasets increases the processing time of Frequent Itemset Mining algorithms, making them unsuitable for many real-world applications. Accordingly, proposing new methods for Frequent Itemset Mining to obtain frequent itemsets in a realistic amount of time is still an open problem. A successful alternative is to employ hardware acceleration using Graphics Processing Units (GPU) and Field Programmable Gates Arrays (FPGA). In this article, a comprehensive review of the state of the art of Frequent Itemsets Mining hardware acceleration is presented. Several approaches (FPGA and GPU based) were contrasted to show their weaknesses and strengths. This survey gathers the most relevant and the latest research efforts for improving the performance of Frequent Itemsets Mining regarding algorithms advances and modern development platforms. Furthermore, this survey organizes the current research on Frequent Itemsets Mining from the hardware perspective considering the source of the data, the development platform, and the baseline algorithm.

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