EFIM: a fast and memory efficient algorithm for high-utility itemset mining

In recent years, high-utility itemset mining has emerged as an important data mining task. However, it remains computationally expensive both in terms of runtime and memory consumption. It is thus an important challenge to design more efficient algorithms for this task. In this paper, we address thi...

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Vydané v:Knowledge and information systems Ročník 51; číslo 2; s. 595 - 625
Hlavní autori: Zida, Souleymane, Fournier-Viger, Philippe, Lin, Jerry Chun-Wei, Wu, Cheng-Wei, Tseng, Vincent S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.05.2017
Springer Nature B.V
Predmet:
Algorithms
Computer memory
Computer Science
Consumption
Data mining
Data Mining and Knowledge Discovery
Database Management
Datasets
Information Storage and Retrieval
Information systems
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Merging
Projection
Regular Paper
Studies
Utilities
Fast Utility Counting, High-utility database merging and projection
Pattern mining
Itemset mining, High-utility mining
ISSN:0219-1377, 0219-3116
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Shrnutí:In recent years, high-utility itemset mining has emerged as an important data mining task. However, it remains computationally expensive both in terms of runtime and memory consumption. It is thus an important challenge to design more efficient algorithms for this task. In this paper, we address this issue by proposing a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discover high-utility itemsets. EFIM relies on two new upper bounds named revised sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper bounds in linear time and space. Moreover, to reduce the cost of database scans, EFIM proposes efficient database projection and transaction merging techniques named High-utility Database Projection and High-utility Transaction Merging (HTM), also performed in linear time. An extensive experimental study on various datasets shows that EFIM is in general two to three orders of magnitude faster than the state-of-art algorithms d 2 HUP, HUI-Miner, HUP-Miner, FHM and UP-Growth+ on dense datasets and performs quite well on sparse datasets. Moreover, a key advantage of EFIM is its low memory consumption.
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ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-016-0986-0