Data mining and knowledge discovery: An analytical investigation

Tal Ben-Zvi, Israel Spiegler

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In recent years, the exponentially growing amount of data made traditional data analysis methods impractical. Knowledge discovery in databases (KDD) provides a framework for alternative methods that address this problem. In this research we follow the KDD process, develop a mathematical model of transforming data and information into knowledge and create a clustering data mining algorithm. To that end, we employ ideas from related, applicable fields (e.g., Operations Research, Inventory Management, and Information Theory). Consequently, we show the merit and value of applying a wellstructured model to knowledge acquisition.

Original languageEnglish
Title of host publicationAssociation for Information Systems - 13th Americas Conference on Information Systems, AMCIS 2007
Subtitle of host publicationReaching New Heights
Pages596-605
Number of pages10
StatePublished - 2007
Event13th Americas Conference on Information Systems, AMCIS 2007 - Keystone, CO, United States
Duration: 10 Aug 200712 Aug 2007

Publication series

NameAssociation for Information Systems - 13th Americas Conference on Information Systems, AMCIS 2007: Reaching New Heights
Volume1

Conference

Conference13th Americas Conference on Information Systems, AMCIS 2007
Country/TerritoryUnited States
CityKeystone, CO
Period10/08/0712/08/07

Keywords

  • Binary representation
  • Data mining
  • Information theory
  • Inventory theory
  • Knowledge discovery process

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