Parallel Mining of Top-k High Utility Itemsets in Spark In-Memory Computing Architecture

Advances in Knowledge Discovery and Data Mining - Lecture Notes in Computer Science ◽

10.1007/978-3-030-16145-3_20 ◽

2019 ◽

pp. 253-265 ◽

Cited By ~ 1

Author(s):

Chun-Han Lin ◽

Cheng-Wei Wu ◽

JianTao Huang ◽

Vincent S. Tseng

Keyword(s):

Computing Architecture ◽

Parallel Mining ◽

High Utility ◽

High Utility Itemsets

Download Full-text

Distributed Mining of Spatial High Utility Itemsets in Very Large Spatiotemporal Databases using Spark In-Memory Computing Architecture

2020 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata50022.2020.9377946 ◽

2020 ◽

Author(s):

R. Uday Kiran ◽

Sadanori Ito ◽

Minh-Son Dao ◽

Koji Zettsu ◽

Cheng-Wei Wu ◽

...

Keyword(s):

Spatiotemporal Databases ◽

Computing Architecture ◽

High Utility ◽

High Utility Itemsets

Download Full-text

Parallel Mining for High Utility Itemsets Mining by Efficient Data Structure

Research and Development on Information and Communication Technology ◽

10.32913/rd-ict.vol3.no14.519 ◽

2017 ◽

Author(s):

Nguyen Manh Hung ◽

Dau Hai Phong

Keyword(s):

Data Mining ◽

Data Structure ◽

Actual Number ◽

Utility Value ◽

Weighted Utility ◽

Parallel Mining ◽

Efficient Data ◽

Transaction Database ◽

High Utility ◽

High Utility Itemsets

Mining high utility itemsets in transaction database is an important task in data mining and widely applied in many areas. Recently, many algorithms have been proposed, but most algorithms for identifying high utility itemsets need to generate candidate sets by overestimating their utility and then calculating their exact utility value. Therefore, the number of candidate itemsets is much larger than the actual number of high utility itemsets. In this paper, we introduce the Retail Transaction-Weighted Utility (RTWU) structure and propose two algorithms: EAHUIMiner algorithm and PEAHUI-Miner parallel algorithm. They have been experimented and compared to the two most efficient algorithms: EFIM and FHM. Results show that our algorithm is better with sparse datasets. DOI: 10.32913/rd-ict.vol3.no14.519

Download Full-text