Data Mining Techniques for Dynamically Classifying and Analyzing Library Database
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Date
2007-02-08
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Publisher
Inflibnet centre
Abstract
Huge amount of data and information is originating in the information era.
Library automation can provide some relief, but data mining techniques
have to be used for dynamically analyzing the library database and to make
strategic decisions for managing the library in an efficient manner. Data
mining is the exploration and analysis of large quantities of data in order to
discover meaningful patterns and rules. Practical data mining can
accomplish a limited set of tasks and only under limited circumstances.
For library, it can play an important role by dynamically analyzing library
database especially data related to the acquisition and circulation. No single
data mining tool and technique is equally applicable. In commercial
application, data mining is usually employed on very large database. This
paper gives the clear picture of some of the most common association rule
data mining techniques which can be applied to the library database and it
outcomes.
Description
Keywords
Data Warehousing, Data Mining, Knowledege Discovery Database, Clustering, Decision Tree, Neural Network, Association Rule