HIGH UTILITY ITEMSETS MINING

2010 ◽  
Vol 09 (06) ◽  
pp. 905-934 ◽  
Author(s):  
YING LIU ◽  
JIANWEI LI ◽  
WEI-KENG LIAO ◽  
ALOK CHOUDHARY ◽  
YONG SHI
Keyword(s):  
Decision Making ◽  
Real World ◽  
Two Phase ◽  
Business Decision ◽  
Memory Space ◽  
Real World Applications ◽  
The Difference ◽  
High Utility ◽  
High Utility Itemsets ◽  
The Impact

High utility itemsets mining identifies itemsets whose utility satisfies a given threshold. It allows users to quantify the usefulness or preferences of items using different values. Thus, it reflects the impact of different items. High utility itemsets mining is useful in decision-making process of many applications, such as retail marketing and Web service, since items are actually different in many aspects in real applications. However, due to the lack of "downward closure property", the cost of candidate generation of high utility itemsets mining is intolerable in terms of time and memory space. This paper presents a Two-Phase algorithm which can efficiently prune down the number of candidates and precisely obtain the complete set of high utility itemsets. The performance of our algorithm is evaluated by applying it to synthetic databases and two real-world applications. It performs very efficiently in terms of speed and memory cost on large databases composed of short transactions, which are difficult for existing high utility itemsets mining algorithms to handle. Experiments on real-world applications demonstrate the significance of high utility itemsets in business decision-making, as well as the difference between frequent itemsets and high utility itemsets.

Research on the Consumer Demand of Decision Making in the E-Commence System Building Process

2011 ◽  
Vol 71-78 ◽  
pp. 2895-2898
Author(s):  
Jian Min Xie ◽  
Qin Qin
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Consumer Satisfaction ◽  
Rough Set Theory ◽  
Consumer Demand ◽  
Successful Implementation ◽  
Business Decision ◽  
Building Process ◽  
Demand Reduction ◽  
The Impact

In the process of developing e-commerce system, enterprises are able to accurately understand and grasp the needs of the user enterprise that is the key to the successful implementation of e-commerce. Based on this, the article proposed a new method which was based on the process of developing consumer demand for e-business decision-making though a rough set theory. This new approach is built using rough set decision model to calculate the different needs of the impact on consumer satisfaction, come to an important degree of each demand and the demand reduction order. This method overcomes the traditional rough set method cumbersome bottlenecks, and helps operating; cases studies show that the proposed method is simple and effective.

Has Dynamic Programming Improved Decision Making?

2019 ◽  
Vol 11 (1) ◽  
pp. 833-858 ◽  
Author(s):  
John Rust
Keyword(s):  
Decision Making ◽  
Dynamic Programming ◽  
Real World ◽  
Operations Research ◽  
Decision Rules ◽  
Decision Problems ◽  
Optimal Decision ◽  
Sequential Decision ◽  
Success Stories ◽  
Real World Applications

Dynamic programming (DP) is a powerful tool for solving a wide class of sequential decision-making problems under uncertainty. In principle, it enables us to compute optimal decision rules that specify the best possible decision in any situation. This article reviews developments in DP and contrasts its revolutionary impact on economics, operations research, engineering, and artificial intelligence with the comparative paucity of its real-world applications to improve the decision making of individuals and firms. The fuzziness of many real-world decision problems and the difficulty in mathematically modeling them are key obstacles to a wider application of DP in real-world settings. Nevertheless, I discuss several success stories, and I conclude that DP offers substantial promise for improving decision making if we let go of the empirically untenable assumption of unbounded rationality and confront the challenging decision problems faced every day by individuals and firms.

The Speed of Business Communication, 1883

1964 ◽  
Vol 38 (3) ◽  
pp. 370-375
Keyword(s):  
Nineteenth Century ◽  
Decision Making ◽  
Business Communication ◽  
Reliable Information ◽  
Business Decision ◽  
Business World ◽  
The Impact

The impact of the telegraph upon the nineteenth-century business world was revolutionary in its magnitude. By economically and swiftly separating communications from transportation, telegraphy increased the flow of reliable information and the pace of business decision-making to a degree unapproached by any previous innovation.

An Empirical Study of Boosting Methods on Severely Imbalanced Data

2014 ◽  
Vol 513-517 ◽  
pp. 2510-2513 ◽  
Author(s):  
Xu Ying Liu
Keyword(s):  
Empirical Study ◽  
Real World ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Real World Applications ◽  
Under Sampling ◽  
The Difference ◽  
Imbalance Learning ◽  
Class Imbalance Learning ◽  
F Measure

Nowadays there are large volumes of data in real-world applications, which poses great challenge to class-imbalance learning: the large amount of the majority class examples and severe class-imbalance. Previous studies on class-imbalance learning mainly focused on relatively small or moderate class-imbalance. In this paper we conduct an empirical study to explore the difference between learning with small or moderate class-imbalance and learning with severe class-imbalance. The experimental results show that: (1) Traditional methods cannot handle severe class-imbalance effectively. (2) AUC, G-mean and F-measure can be very inconsistent for severe class-imbalance, which seldom appears when class-imbalance is moderate. And G-mean is not appropriate for severe class-imbalance learning because it is not sensitive to the change of imbalance ratio. (3) When AUC and G-mean are evaluation metrics, EasyEnsemble is the best method, followed by BalanceCascade and under-sampling. (4) A little under-full balance is better for under-sampling to handle severe class-imbalance. And it is important to handle false positives when design methods for severe class-imbalance.

PROVIDING TIMELY UPDATED SEQUENTIAL PATTERNS IN DECISION MAKING

2010 ◽  
Vol 09 (06) ◽  
pp. 873-888 ◽  
Author(s):  
TZUNG-PEI HONG ◽  
CHING-YAO WANG ◽  
CHUN-WEI LIN
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Real World ◽  
Experimental Results ◽  
Sequential Patterns ◽  
The Past ◽  
Large Databases ◽  
Real World Applications ◽  
Critical Task ◽  
Maintenance Process

Mining knowledge from large databases has become a critical task for organizations. Managers commonly use the obtained sequential patterns to make decisions. In the past, databases were usually assumed to be static. In real-world applications, however, transactions may be updated. In this paper, a maintenance algorithm for rapidly updating sequential patterns for real-time decision making is proposed. The proposed algorithm utilizes previously discovered large sequences in the maintenance process, thus greatly reducing the number of database rescans and improving performance. Experimental results verify the performance of the proposed approach. The proposed algorithm provides real-time knowledge that can be used for decision making.

Conclusion

2018 ◽  
Author(s):  
Patrick Weller
Keyword(s):  
Decision Making ◽  
New Zealand ◽  
Political Systems ◽  
Prime Ministers ◽  
The Difference ◽  
The Impact

The conclusion first assesses the prime ministers against the criteria set out in the introduction: their longevity, their control over their parties, and their ability to shape the agenda. The first two can provide evidence of those who were successful. Noticeably those who brought their party from opposition to government were those who were likely to flourish. Second, the conclusion explores the difference between the four political systems and the impact they have on the working of the prime ministers. It identifies the variations in cabinet practices and the degree to which cabinet remains a consistent decision-making forum in Australia and New Zealand but less so in Britain and Canada. It concludes by stressing that much of the difference can be explained by the levels of accountability prime ministers have to their parliamentary colleagues, rather than a broader party electorate: a choice between competing principles of party democracy and accountability.

Ordering policy estimation for high utility item-sets considering negative item values in large databases

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Data Mining ◽  
Real World ◽  
Defective Items ◽  
Utility Mining ◽  
Mining Algorithm ◽  
Negative Item ◽  
Ordering Policy ◽  
Large Databases ◽  
Real World Applications ◽  
High Utility

Utility mining with negative item values has recently received interest in the data mining field due to its practical considerations. Previously, the values of utility item-sets have been taken into consideration as positive. However, in real-world applications an item-set may be related to negative item values. This paper presents a method for redesigning the ordering policy by including high utility item-sets with negative items. Initially, utility mining algorithm is used to find high utility item-sets. Then, ordering policy is estimated for high utility items considering defective and non-defective items. A numerical example is illustrated to validate the results

scholarly journals Emotions and decisions in the real world: What can we learn from quasi-field experiments?

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243044
Author(s):  
Syon P. Bhanot ◽  
Daphne Chang ◽  
Julia Lee Cunningham ◽  
Matthew Ranson
Keyword(s):  
Decision Making ◽  
Field Experiment ◽  
Real World ◽  
Field Experiments ◽  
Negative Emotion ◽  
Sports Fans ◽  
The Social ◽  
Powerful Approach ◽  
The Impact ◽  
Biased Estimates

Researchers in the social sciences have increasingly studied how emotions influence decision-making. We argue that research on emotions arising naturally in real-world environments is critical for the generalizability of insights in this domain, and therefore to the development of this field. Given this, we argue for the increased use of the “quasi-field experiment” methodology, in which participants make decisions or complete tasks after as-if-random real-world events determine their emotional state. We begin by providing the first critical review of this emerging literature, which shows that real-world events provide emotional shocks that are at least as strong as what can ethically be induced under laboratory conditions. However, we also find that most previous quasi-field experiment studies use statistical techniques that may result in biased estimates. We propose a more statistically-robust approach, and illustrate it using an experiment on negative emotion and risk-taking, in which sports fans completed risk-elicitation tasks immediately after watching a series of NFL games. Overall, we argue that when appropriate statistical methods are used, the quasi-field experiment methodology represents a powerful approach for studying the impact of emotion on decision-making.

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