Modified Frequent Pattern Tree by Balakrishna Eppakayala (8 results)

Condition: New.

Taschenbuch. Condition: Neu. Modified Frequent Pattern Tree for Mining Negative Association Rules | A Data Mining Techniques for Market Basket Analysis | Eppakayala Balakrishna (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786204979540 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Condition: New. Print on Demand.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Mining of Negative Association Rule is helpful to discover the competent items i.e., one item can be substituted for another items in a database. In decision making, we generally use many advantage factors, and also consider some disadvantage factors, for the intention of less-risk (more-profit). It is necessary to focus on Negative Association Rule mining, because in the super market data analysis, Negative Association Rule Mining identifies whatever the items which are not buying together and also whatever the items which may be used as replacing (substituted or alternative) items for other items. Such kind of analysis is possible through Negative Association Rule Mining. Negative rules specify that the relationship between the present and absent item-sets. Negative Association Rule mining is also important because such types of rules should be considered in decision making for applications include product placement and investment analysis. 124 pp. Englisch.

Condition: New. PRINT ON DEMAND.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Balakrishna EppakayalaDr. Eppakayala Balakrishna completed M.Tech and Ph.D in CSE from JNTU University, Hyderabad. He is working as an Associate Professor in the department of CSE, Vaagdevi College of Engineering.Mining of Negati.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Mining of Negative Association Rule is helpful to discover the competent items i.e., one item can be substituted for another items in a database. In decision making, we generally use many advantage factors, and also consider some disadvantage factors, for the intention of less-risk (more-profit). It is necessary to focus on Negative Association Rule mining, because in the super market data analysis, Negative Association Rule Mining identifies whatever the items which are not buying together and also whatever the items which may be used as replacing (substituted or alternative) items for other items. Such kind of analysis is possible through Negative Association Rule Mining. Negative rules specify that the relationship between the present and absent item-sets. Negative Association Rule mining is also important because such types of rules should be considered in decision making for applications include product placement and investment analysis.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 124 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Mining of Negative Association Rule is helpful to discover the competent items i.e., one item can be substituted for another items in a database. In decision making, we generally use many advantage factors, and also consider some disadvantage factors, for the intention of less-risk (more-profit). It is necessary to focus on Negative Association Rule mining, because in the super market data analysis, Negative Association Rule Mining identifies whatever the items which are not buying together and also whatever the items which may be used as replacing (substituted or alternative) items for other items. Such kind of analysis is possible through Negative Association Rule Mining. Negative rules specify that the relationship between the present and absent item-sets. Negative Association Rule mining is also important because such types of rules should be considered in decision making for applications include product placement and investment analysis.