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Article

YALE: rapid prototyping for complex data mining tasks

Authors:

Ralf Klinkenberg,

Timm EulerAuthors Info & Claims

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 935 - 940

https://doi.org/10.1145/1150402.1150531

Published: 20 August 2006 Publication History

Abstract

KDD is a complex and demanding task. While a large number of methods has been established for numerous problems, many challenges remain to be solved. New tasks emerge requiring the development of new methods or processing schemes. Like in software development, the development of such solutions demands for careful analysis, specification, implementation, and testing. Rapid prototyping is an approach which allows crucial design decisions as early as possible. A rapid prototyping system should support maximal re-use and innovative combinations of existing methods, as well as simple and quick integration of new ones.This paper describes Yale, a free open-source environment forKDD and machine learning. Yale provides a rich variety of methods whichallows rapid prototyping for new applications and makes costlyre-implementations unnecessary. Additionally, Yale offers extensive functionality for process evaluation and optimization which is a crucial property for any KDD rapid prototyping tool. Following the paradigm of visual programming eases the design of processing schemes. While the graphical user interface supports interactive design, the underlying XML representation enables automated applications after the prototyping phase.After a discussion of the key concepts of Yale, we illustrate the advantages of rapid prototyping for KDD on case studies ranging from data pre-processing to result visualization. These case studies cover tasks like feature engineering, text mining, data stream mining and tracking drifting concepts, ensemble methods and distributed data mining. This variety of applications is also reflected in a broad user base, we counted more than 40,000 downloads during the last twelve months.

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https://doi.org/10.1371/journal.pcbi.1012574
Weber TMayer S(2024)Usability and Adoption of Graphical Tools for Data-Driven DevelopmentProceedings of Mensch und Computer 202410.1145/3670653.3670658(231-241)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1145/3670653.3670658
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Index Terms

YALE: rapid prototyping for complex data mining tasks
1. Computing methodologies
  1. Machine learning

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Information

Published In

cover image ACM Conferences

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2006

986 pages

ISBN:1595933395

DOI:10.1145/1150402

Conference Chair:
Tina Eliassi-Rad
LLNL
,
General Chair:
Lyle Ungar
University of Pennsylvania
,
Program Chairs:
Mark Craven
University of Wisconsin
,
Dimitrios Gunopulos
University of California, Riverside

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2006

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Conference

KDD06

Sponsor:

KDD06: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 20 - 23, 2006

PA, Philadelphia, USA

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

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https://doi.org/10.1371/journal.pone.0311255
Demšar JZupan B(2024)Hands-on training about data clustering with orange data mining toolboxPLOS Computational Biology10.1371/journal.pcbi.101257420:12(e1012574)Online publication date: 18-Dec-2024
https://doi.org/10.1371/journal.pcbi.1012574
Weber TMayer S(2024)Usability and Adoption of Graphical Tools for Data-Driven DevelopmentProceedings of Mensch und Computer 202410.1145/3670653.3670658(231-241)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1145/3670653.3670658
Weber TEhe JMayer S(2024)Extending Jupyter with Multi-Paradigm EditorsProceedings of the ACM on Human-Computer Interaction10.1145/36602478:EICS(1-22)Online publication date: 17-Jun-2024
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Palit HGunawan AJeremia D(2024)SparkFlow: A Simple Big Data Analysis Application over Apache Spark2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA)10.1109/ICTIIA61827.2024.10761597(1-6)Online publication date: 12-Sep-2024
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Indihar Štemberger MBosilj Vuksic VMorelli FJaklič J(2024)Exploring the role of new and enhanced BPM capabilities in customer experience management: does BPM matter?Business Process Management Journal10.1108/BPMJ-10-2023-083830:8(120-143)Online publication date: 29-May-2024
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Batran RAhmed EAwad HAbdel Latif N(2024)Naturally based pyrazoline derivatives as aminopeptidase N, VEGFR2 and MMP9 inhibitors: design, synthesis and molecular modelingRSC Advances10.1039/D4RA01801J14:31(22434-22448)Online publication date: 2024
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Hefft DGroße NKreyenborg AKiklhorn DHenke M(2024)Approaches And Research Directions For Adapting Rapid Prototyping In Industrial Service Development: A Systematic Literature ReviewProcedia CIRP10.1016/j.procir.2024.10.130130(562-572)Online publication date: 2024
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Salo MPuustinen SJounela PHänninen HHiedanpää J(2024)The art of the ‘common good’: Property and nature values in strategic land-use planning in FinlandEnvironmental Science & Policy10.1016/j.envsci.2024.103815159(103815)Online publication date: Sep-2024
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