Efficient mining of high average-utility itemsets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10236456" target="_blank" >RIV/61989100:27240/17:10236456 - isvavai.cz</a>

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RIV/61989100:27740/17:10236456

Výsledek na webu

<a href="https://www.taylorfrancis.com/books/e/9781498779456/chapters/10.1201%2F9781315375083-37" target="_blank" >https://www.taylorfrancis.com/books/e/9781498779456/chapters/10.1201%2F9781315375083-37</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Efficient mining of high average-utility itemsets

Popis výsledku v původním jazyce

In traditional High-Utility Itemset Mining (HUIM), the utility of an itemset is defined as the sum of the utilities of its items in transactions where it appears. An important problem with this definition is that it does not take itemset length into account. To provide a better assessment of each itemset’s utility, the task of High Average-Utility Itemset Mining (HAUIM) was proposed and several algorithms have been extensively studied. Most of the past works are based on level-wise or patterngrowth approaches, which required amounts of computation to mine the required High Average-Utility Itemsets (HAUIs). In this paper, we present an efficient Average-Utility (AU)-list structure to discover the HAUIs more efficiently. A depth-first search algorithm named HAUI-Miner is proposed to explore the search space without candidate generation, and an efficient pruning strategy is developed to reduce the search space and speed up the mining process. Extensive experiments are conducted to compare the performance of HAUI-Miner with the state-of-the-art algorithms of HAUIM in terms of runtime and number of determining nodes.

Název v anglickém jazyce

Efficient mining of high average-utility itemsets

Popis výsledku anglicky

In traditional High-Utility Itemset Mining (HUIM), the utility of an itemset is defined as the sum of the utilities of its items in transactions where it appears. An important problem with this definition is that it does not take itemset length into account. To provide a better assessment of each itemset’s utility, the task of High Average-Utility Itemset Mining (HAUIM) was proposed and several algorithms have been extensively studied. Most of the past works are based on level-wise or patterngrowth approaches, which required amounts of computation to mine the required High Average-Utility Itemsets (HAUIs). In this paper, we present an efficient Average-Utility (AU)-list structure to discover the HAUIs more efficiently. A depth-first search algorithm named HAUI-Miner is proposed to explore the search space without candidate generation, and an efficient pruning strategy is developed to reduce the search space and speed up the mining process. Extensive experiments are conducted to compare the performance of HAUI-Miner with the state-of-the-art algorithms of HAUIM in terms of runtime and number of determining nodes.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Communication, Management and Information Technology - Proceedings of the International Conference on Communication, Management and Information Technology, ICCMIT 2016

ISBN

978-1-315-37508-3

ISSN

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e-ISSN

neuvedeno

Počet stran výsledku

8

Strana od-do

241-248

Název nakladatele

CRC Press

Místo vydání

Leiden

Místo konání akce

Cosenza

Datum konání akce

26. 4. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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