TY - GEN
T1 - Data mining and knowledge discovery
T2 - 13th Americas Conference on Information Systems, AMCIS 2007
AU - Ben-Zvi, Tal
AU - Spiegler, Israel
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Binary representation
KW - Data mining
KW - Information theory
KW - Inventory theory
KW - Knowledge discovery process
UR - http://www.scopus.com/inward/record.url?scp=84870163068&partnerID=8YFLogxK
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AN - SCOPUS:84870163068
SN - 9781604233810
T3 - Association for Information Systems - 13th Americas Conference on Information Systems, AMCIS 2007: Reaching New Heights
SP - 596
EP - 605
BT - Association for Information Systems - 13th Americas Conference on Information Systems, AMCIS 2007
Y2 - 10 August 2007 through 12 August 2007
ER -