Data Mining Techniques for Dynamically Classifying and Analyzing Library Database
| dc.contributor.author | Dwivedi, Roopesh Kumar | |
| dc.contributor.author | Bajpani, R P | |
| dc.date.accessioned | 2010-05-21T06:28:57Z | |
| dc.date.available | 2010-05-21T06:28:57Z | |
| dc.date.issued | 2007-02-08 | |
| dc.description.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. | en_US |
| dc.identifier.isbn | 978-81-902079-4-2 | |
| dc.identifier.uri | http://hdl.handle.net/1944/1424 | |
| dc.language.iso | en | en_US |
| dc.publisher | Inflibnet centre | en_US |
| dc.subject | Data Warehousing | en_US |
| dc.subject | Data Mining | en_US |
| dc.subject | Knowledege Discovery Database | en_US |
| dc.subject | Clustering | en_US |
| dc.subject | Decision Tree | en_US |
| dc.subject | Neural Network | en_US |
| dc.subject | Association Rule | en_US |
| dc.title | Data Mining Techniques for Dynamically Classifying and Analyzing Library Database | en_US |
| dc.type | Article | en_US |