scholarly journals Incrementally updating the high average-utility patterns with pre-large concept

2020 ◽  
Vol 50 (11) ◽  
pp. 3788-3807
Author(s):  
Jerry Chun-Wei Lin ◽  
Matin Pirouz ◽  
Youcef Djenouri ◽  
Chien-Fu Cheng ◽  
Usman Ahmed
Keyword(s):  
State Of The Art ◽  
The State ◽  
Batch Mode ◽  
Itemset Mining ◽  
The Past ◽  
Dynamic Databases ◽  
Speed Up ◽  
Average Utility ◽  
High Utility ◽  
High Utility Patterns

Abstract High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset (HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction insertion in dynamic databases is developed, which relies on the average-utility-list (AUL) structures. Moreover, we apply the pre-large concept on HAUIM. A pre-large concept is used to speed up the mining performance, which can ensure that if the total utility in the newly inserted transaction is within the safety bound, the small itemsets in the original database could not be the large ones after the database is updated. This, in turn, reduces the recurring database scans and obtains the correct HAUIs. Experiments demonstrate that the PRE-HAUIMI outperforms the state-of-the-art batch mode HAUI-Miner, and the state-of-the-art incremental IHAUPM and FUP-based algorithms in terms of runtime, memory, number of assessed patterns and scalability.

scholarly journals Dynamic maintenance model for high average-utility pattern mining with deletion operation

2021 ◽  
Author(s):  
Jimmy Ming-Tai Wu ◽  
Qian Teng ◽  
Shahab Tayeb ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Pattern Mining ◽  
Computational Cost ◽  
Practical Applications ◽  
Itemset Mining ◽  
Dynamic Databases ◽  
Speed Up ◽  
Dynamic Maintenance ◽  
Average Utility ◽  
High Utility ◽  
Maintenance Model

AbstractThe high average-utility itemset mining (HAUIM) was established to provide a fair measure instead of genetic high-utility itemset mining (HUIM) for revealing the satisfied and interesting patterns. In practical applications, the database is dynamically changed when insertion/deletion operations are performed on databases. Several works were designed to handle the insertion process but fewer studies focused on processing the deletion process for knowledge maintenance. In this paper, we then develop a PRE-HAUI-DEL algorithm that utilizes the pre-large concept on HAUIM for handling transaction deletion in the dynamic databases. The pre-large concept is served as the buffer on HAUIM that reduces the number of database scans while the database is updated particularly in transaction deletion. Two upper-bound values are also established here to reduce the unpromising candidates early which can speed up the computational cost. From the experimental results, the designed PRE-HAUI-DEL algorithm is well performed compared to the Apriori-like model in terms of runtime, memory, and scalability in dynamic databases.

scholarly journals MINING OF HIGH-UTILITY ITEMSETS WITH NEGATIVE UTILITY

2020 ◽  
Vol 1 (2) ◽  
pp. 44-47
Author(s):  
Tung N.T ◽  
Nguyen Le Van ◽  
Trinh Cong Nhut ◽  
Tran Van Sang
Keyword(s):  
State Of The Art ◽  
Upper Bounds ◽  
Itemset Mining ◽  
Novel Structure ◽  
Transactional Databases ◽  
Speed Up ◽  
Projection Techniques ◽  
High Utility ◽  
High Utility Itemsets ◽  
Mining Algorithms

The goal of the high-utility itemset mining task is to discover combinations of items that yield high profits from transactional databases. HUIM is a useful tool for retail stores to analyze customer behaviors. However, in the real world, items are found with both positive and negative utility values. To address this issue, we propose an algorithm named Modified Efficient High‐utility Itemsets mining with Negative utility (MEHIN) to find all HUIs with negative utility. This algorithm is an improved version of the EHIN algorithm. MEHIN utilizes 2 new upper bounds for pruning, named revised subtree and revised local utility. To reduce dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. An array‐based utility‐counting technique is also utilized to calculate upper‐bound efficiently. The MEHIN employs a novel structure called P-set to reduce the number of transaction scans and to speed up the mining process. Experimental results show that the proposed algorithms considerably outperform the state-of-the-art HUI-mining algorithms on negative utility in retail databases in terms of runtime.

scholarly journals The state of the art in soybean transcriptomics resources and gene coexpression networks

2021 ◽  
Author(s):  
Fabricio Almeida-Silva ◽  
Kanhu C Moharana ◽  
Thiago M Venancio
Keyword(s):  
State Of The Art ◽  
The State ◽  
Gene Coexpression Network ◽  
Rna Seq ◽  
Transcriptomic Data ◽  
The Past ◽  
Gene Coexpression ◽  
Genomics Research ◽  
Public Repositories ◽  
Coexpression Networks

Abstract In the past decade, over 3000 samples of soybean transcriptomic data have accumulated in public repositories. Here, we review the state of the art in soybean transcriptomics, highlighting the major microarray and RNA-seq studies that investigated soybean transcriptional programs in different tissues and conditions. Further, we propose approaches for integrating such big data using gene coexpression network and outline important web resources that may facilitate soybean data acquisition and analysis, contributing to the acceleration of soybean breeding and functional genomics research.

The State of the Art in Management Education Applicable to Aerospace Activities

1967 ◽  
Vol 71 (677) ◽  
pp. 342-343
Author(s):  
F. H. East
Keyword(s):  
Research And Development ◽  
Management Education ◽  
State Of The Art ◽  
Development Project ◽  
The State ◽  
The Other ◽  
The Past ◽  
Defence Budget ◽  
The One ◽  
Almost All

The Aviation Group of the Ministry of Technology (formerly the Ministry of Aviation) is responsible for spending a large part of the country's defence budget, both in research and development on the one hand and production or procurement on the other. In addition, it has responsibilities in many non-defence fields, mainly, but not exclusively, in aerospace.Few developments have been carried out entirely within the Ministry's own Establishments; almost all have required continuous co-operation between the Ministry and Industry. In the past the methods of management and collaboration and the relative responsibilities of the Ministry and Industry have varied with time, with the type of equipment to be developed, with the size of the development project and so on. But over the past ten years there has been a growing awareness of the need to put some system into the complex business of translating a requirement into a specification and a specification into a product within reasonable bounds of time and cost.

Cultural Economics: The State of the Art and Perspectives

2021 ◽  
Vol 27 (1) ◽  
pp. 7-32
Author(s):  
Bruce A. Seaman
Keyword(s):  
Industrial Organization ◽  
Intellectual Development ◽  
State Of The Art ◽  
Cultural Economics ◽  
The State ◽  
Sports Economics ◽  
Developmental Patterns ◽  
The Past ◽  
Alternative Perspective ◽  
Core Questions

The intellectual development of cultural economics has exhibited some notable similarities to the challenges faced by researchers pioneering in other areas of economics. While this is not really surprising, previous reviews of this literature have not focused on such patterns. Specifically, the methodology and normative implications of the field of industrial organization and antitrust policy suggest a series of stages identified here as foundation, maturation, reevaluation, and backlash that suggest a way of viewing the development of and controversies surrounding cultural economics. Also, the emerging field of sports economics, which already shares some substantive similarities to the questions addressed in cultural economics, presents a pattern of development by which core questions and principles are identified in a fragmented literature, which then slowly coalesces and becomes consolidated into a more unified literature that essentially reconfirms and extends those earlier core principles. This fragmentation and consolidation pattern is also exhibited by the development of cultural economics. While others could surely suggest different parallels in the search for such developmental patterns, this way of organizing ones thinking about the past and future of this field provides a hoped for alternative perspective on the state of the art of cultural economics.

scholarly journals The Embeddedness of Social Sciences and Economics in Research on Resources

Resources ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 15
Author(s):  
Juan Uribe-Toril ◽  
José Luis Ruiz-Real ◽  
Jaime de Pablo Valenciano
Keyword(s):  
Social Sciences ◽  
State Of The Art ◽  
Local Development ◽  
The State ◽  
Special Issue ◽  
Continuous Review ◽  
The Past ◽  
Research Fields ◽  
Wide Range ◽  
Knowledge Area

Sustainability, local development, and ecology are keywords that cover a wide range of research fields in both experimental and social sciences. The transversal nature of this knowledge area creates synergies but also divergences, making a continuous review of the existing literature necessary in order to facilitate research. There has been an increasing number of articles that have analyzed trends in the literature and the state-of-the-art in many subjects. In this Special Issue of Resources, the most prestigious researchers analyzed the past and future of Social Sciences in Resources from an economic, social, and environmental perspective.

scholarly journals Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 37 ◽  
Author(s):  
Luca Cappelletti ◽  
Tommaso Fontana ◽  
Guido Walter Di Donato ◽  
Lorenzo Di Tucci ◽  
Elena Casiraghi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Missing Data ◽  
State Of The Art ◽  
The State ◽  
Complex Data ◽  
Data Imputation ◽  
Genome Sequences ◽  
Missing Data Imputation ◽  
The Past ◽  
Learning Techniques

Missing data imputation has been a hot topic in the past decade, and many state-of-the-art works have been presented to propose novel, interesting solutions that have been applied in a variety of fields. In the past decade, the successful results achieved by deep learning techniques have opened the way to their application for solving difficult problems where human skill is not able to provide a reliable solution. Not surprisingly, some deep learners, mainly exploiting encoder-decoder architectures, have also been designed and applied to the task of missing data imputation. However, most of the proposed imputation techniques have not been designed to tackle “complex data”, that is high dimensional data belonging to datasets with huge cardinality and describing complex problems. Precisely, they often need critical parameters to be manually set or exploit complex architecture and/or training phases that make their computational load impracticable. In this paper, after clustering the state-of-the-art imputation techniques into three broad categories, we briefly review the most representative methods and then describe our data imputation proposals, which exploit deep learning techniques specifically designed to handle complex data. Comparative tests on genome sequences show that our deep learning imputers outperform the state-of-the-art KNN-imputation method when filling gaps in human genome sequences.

Discovering the Past for Yourself

2017 ◽  
Author(s):  
Jukka Tyrkkö
Keyword(s):  
Historical Data ◽  
State Of The Art ◽  
Language Teaching ◽  
Statistical Significance ◽  
The State ◽  
The Past ◽  
History Of ◽  
Digital Pedagogy ◽  
Basic Concepts ◽  
Corpus Tools

This chapter outlines the state of the art in corpus-based language teaching and digital pedagogy, focusing on the differences between using corpora with present-day and historical data. The basic concepts of corpus-based research such as representativeness, frequency, and statistical significance can be introduced to students who are new to corpus methods, and the application of these concepts to the history of English can deepen students’ understanding of how historical varieties of the language are researched. This chapter will also address some of the key challenges particular to teaching the history of English using corpora, such as dealing with the seemingly counterintuitive findings, non-standard features, and small datasets. Finally, following an overview of available historical corpora and corpus tools, several practical examples of corpus-driven activities will be discussed in detail, with suggestions and ideas on how a teacher might prepare and run corpus-based lessons.

Financial crime – is there any way out of the theoretical deadlock?

2017 ◽  
Vol 24 (4) ◽  
pp. 529-540
Author(s):  
Paul Eisenberg
Keyword(s):  
Design Methodology ◽  
State Of The Art ◽  
White Collar Crime ◽  
White Collar ◽  
The State ◽  
Financial Crime ◽  
Content Type ◽  
The Past ◽  
Financial Scandals ◽  
Research Questions

Purpose This paper aims to approach fundamental topics of financial crime and the law. What does constitute financial crime? Which field of law is best suited to address the threats of transgression by financial executives? What does motivate highly rewarded financiers to become white collar criminals? Design/methodology/approach To answer these research questions, contemporary theories of criminology in general and of white collar crime in particular, as well as theories on motivation, are critically discussed. Benefits and limitations of the theories in use are exemplified on the background of the London Interbank Offered Rate (LIBOR) scandal. Findings The paper criticises that the state-of-the-art theories are not able to embrace financial criminality in its entirety. A provoking pace for further research might be that of psychopathic disorders among white collar criminals. Thus, white collar crime maintains its challenging character. Originality/value This paper provides a thorough testing of multidisciplinary theories that emerged over the past decades against the recent LIBOR scandal. The research questions addressed and the methodologies applied provide a framework for the assessment of the prevailing theories against other financial scandals.

Intellectual Foundations of Current Research in Family Business: An Identification and Review of 25 Influential Articles

2009 ◽  
Vol 23 (1) ◽  
pp. 9-26 ◽  
Author(s):  
James J. Chrisman ◽  
Franz W. Kellermanns ◽  
Kam C. Chan ◽  
Kartono Liano
Keyword(s):  
Citation Analysis ◽  
Family Business ◽  
State Of The Art ◽  
Family Businesses ◽  
The State ◽  
Current Understanding ◽  
Future Research ◽  
The Past ◽  
Intellectual Foundations

This article identifies 25 articles that have been particularly influential in shaping the state of the art of research on family businesses. These works were identified based on a citation analysis of family business articles published over the past 6 years in the four journals that publish most of the research. The authors summarize those influential studies and discuss their most important contributions to scholars’ current understanding of family business. By identifying common themes among those studies, the authors are able to provide directions for future research in the field.

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