An efficient algorithm to mine high average-utility itemsets
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86098019" target="_blank" >RIV/61989100:27240/16:86098019 - isvavai.cz</a>
Výsledek na webu
<a href="http://www.sciencedirect.com/science/article/pii/S1474034616300507" target="_blank" >http://www.sciencedirect.com/science/article/pii/S1474034616300507</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.aei.2016.04.002" target="_blank" >10.1016/j.aei.2016.04.002</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
An efficient algorithm to mine high average-utility itemsets
Popis výsledku v původním jazyce
With the ever increasing number of applications of data mining, high-utility itemset mining (HUIM) has become a critical issue in recent decades. In traditional 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. Because the utility of larger itemset is generally greater than the utility of smaller itemset, traditional HUIM algorithms tend to be biased toward finding a set of large itemsets. Thus, this definition is not a fair measurement of utility. To provide a better assessment of each itemset's utility, the task of high average-utility itemset mining (HAUIM) was proposed. It introduces the average utility measure, which considers both the length of itemsets and their utilities, and is thus more appropriate in real-world situations. Several algorithms have been designed for this task. They can be generally categorized as either level-wise or pattern-growth approaches. Both of them require, however, the amount of computation to find the actual high average-utility itemsets (HAUls). 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 HAUIM algorithms in terms of runtime, number of determining nodes, memory usage and scalability.
Název v anglickém jazyce
An efficient algorithm to mine high average-utility itemsets
Popis výsledku anglicky
With the ever increasing number of applications of data mining, high-utility itemset mining (HUIM) has become a critical issue in recent decades. In traditional 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. Because the utility of larger itemset is generally greater than the utility of smaller itemset, traditional HUIM algorithms tend to be biased toward finding a set of large itemsets. Thus, this definition is not a fair measurement of utility. To provide a better assessment of each itemset's utility, the task of high average-utility itemset mining (HAUIM) was proposed. It introduces the average utility measure, which considers both the length of itemsets and their utilities, and is thus more appropriate in real-world situations. Several algorithms have been designed for this task. They can be generally categorized as either level-wise or pattern-growth approaches. Both of them require, however, the amount of computation to find the actual high average-utility itemsets (HAUls). 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 HAUIM algorithms in terms of runtime, number of determining nodes, memory usage and scalability.
Klasifikace
Druh
J<sub>x</sub> - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)
CEP obor
IN - Informatika
OECD FORD obor
—
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2016
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Advanced Engineering Informatics
ISSN
1474-0346
e-ISSN
—
Svazek periodika
30
Číslo periodika v rámci svazku
2
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
11
Strana od-do
233-243
Kód UT WoS článku
000376694600011
EID výsledku v databázi Scopus
2-s2.0-84963640907