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Technique without theory or theory from technique? An examination of practical, philosophical, and foundational issues in data mining

机译:没有理论的技术还是来自技术的理论?考察数据挖掘中的实践,哲学和基础问题

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摘要

In this paper, it is argued that although data mining (DM) is being touted as a solution to many business problems and is basking in the glory of electronic business environments of today, as practiced currently, it reflects a preoccupation with short-run commercial applications and a neglect of the underlying theoretical issues. Although an argument can be made that theoretical precedence is not a necessary prerequisite for practical application or for commercial success, it can also be argued that an exclusive reliance on data-driven and exploratory components of pattern recognition without a corresponding attention to the causal schemas underlying patterns is destined to limit the potential for DM to evolve into a long-term solution to business problems or into an intellectual discipline in its own right. This paper presents an overview of key features and assumptions in DM and examines some of the key practical, philosophical, and foundational issues in DM.
机译:本文认为,尽管数据挖掘(DM)被吹捧为解决许多业务问题的解决方案,并且以当今的电子商务环境为基础,但正如目前所实践的那样,它反映了人们对短期商业的关注应用和对基本理论问题的忽视。尽管可以说理论优先级不是实际应用或商业成功的必要先决条件,但也可以争辩说,模式识别的唯一依赖是数据驱动和探索性组件,而没有相应地关注基础的因果图式这种模式注定会限制DM发展成为业务问题的长期解决方案或本身发展为知识学科的潜力。本文概述了DM中的关键功能和假设,并研究了DM中的一些关键实践,哲学和基础问题。

著录项

  • 来源
    《AI & society》 |2007年第3期|p.347-355|共9页
  • 作者

    Appa Rao Korukonda;

  • 作者单位

    Department of Computer & Management Information Systems, College of Business, Bloomsburg University of Pennsylvania, Room 247 Sutliff Hall, 400 East Second Street, Bloomsburg, PA17815-1301, USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;
  • 关键词

  • 入库时间 2022-08-17 13:04:20

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