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Third IEEE International Conference on Data Mining, 2004
Traditional association rule mining algorithms only generate a large number of highly frequent rules, but these rules do not provide useful answers for what the high utility rules are. We develop a novel idea of top-K objective-directed data mining, which focuses on mining the top-K high utility closed patterns that directly support a given business ...
Raymond Chan +2 more
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Traditional association rule mining algorithms only generate a large number of highly frequent rules, but these rules do not provide useful answers for what the high utility rules are. We develop a novel idea of top-K objective-directed data mining, which focuses on mining the top-K high utility closed patterns that directly support a given business ...
Raymond Chan +2 more
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High-utility and diverse itemset mining
Applied Intelligence, 2021High-utility Itemset Mining (HUIM) finds patterns from a transaction database with their utility no less than a user-defined threshold. The utility of an itemset is defined as the sum of the utilities of its items. The utility notion enables a data analyst to associate a profit score with each item and thereof to a pattern. We extend the notion of high-
Amit Verma +4 more
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FULL-RCMA: A High Utilization EPON
IEEE Journal on Selected Areas in Communications, 2004This paper proposes an alternate solution for Ethernet passive optical networks. Our solution uses a novel protocol named full utilization local loop request contention multiple-access protocol to efficiently provide communications in passive optical networks.
Chuan Heng Foh +3 more
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Efficient Method for Mining High-Utility Itemsets Using High-Average Utility Measure
2020Mining high-utility itemsets (HUIs) based on high-average utility measure is an important task in the data mining field. However, many of the existing algorithms are performing the mining process sequentially and do not utilize the widely available multi-core processors, thus requiring long execution times. To address this issue, we propose an extended
Nguyen Thi Thuy Loan +6 more
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Mining High-Utility Itemsets with Multiple Minimum Utility Thresholds
Proceedings of the Eighth International C* Conference on Computer Science & Software Engineering - C3S2E '15, 2008High-utility itemset mining (HUIM) is an emerging topic in data mining. It consists of discovering high-utility itemsets (HUIs), i.e. groups of items (itemsets) that generate a high profit in transactional databases. Several algorithms have been proposed for this task.
Jerry Chun-Wei Lin +3 more
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High utility itemsets mining with negative utility value: A survey
Journal of Intelligent & Fuzzy Systems, 2018Mining high utility itemsets (HUIs) is a basic task of frequent itemsets mining (FIM). In recent years, a trend in FIM has been to design algorithm for mining HUIs because FIM assumes that each item can not appear more than once in a transaction and all items have the same importance (weight, unit profit, price, etc.).
Kuldeep Singh 0003 +3 more
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Utilizing the power of high-performance computing
IEEE Signal Processing Magazine, 1998The main focus of this article is the design of embedded signal processing (ESP) application software. We identify the characteristics of such applications in terms of their computational requirements, data layouts, and latency and throughput constraints. We describe an ESP application, an adaptive sonar beamformer. Then, we briefly survey the state-of-
Wenheng Liu, Viktor K. Prasanna
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Vertical mining for high utility itemsets
2012 IEEE International Conference on Granular Computing, 2012Recently, high utility itemsets mining becomes one of the most important research issues in data mining due to its ability to consider different profit values for every item. In the past studies, most algorithms generate high utility itemsets from a set of transactions in horizontal data format.
Wei Song 0004, Yu Liu, Jinhong Li
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Mining high average-utility itemsets
2009 IEEE International Conference on Systems, Man and Cybernetics, 2009The average utility measure is adopted in this paper to reveal a better utility effect of combining several items than the original utility measure. A mining algorithm is then proposed to efficiently find the high average-utility itemsets. It uses the summation of the maximal utility among the items in each transaction including the target itemset as ...
Tzung-Pei Hong +2 more
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The DOGMA approach to high-utilization supercomputing
Proceedings. The Seventh International Symposium on High Performance Distributed Computing (Cat. No.98TB100244), 2002Heterogeneous distributed computing has traditionally been a problematic undertaking which increases in complexity as heterogeneity increases. The recent advent of Java has made heterogeneous computing a fairly straightforward task. Nevertheless, many researchers have not considered the use of Java in a mainstream parallel programming environment.
Glenn Judd, Mark J. Clement, Quinn Snell
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