%0 Journal Article
%T DataAprori algorithm : Implementation of scalable Data Mining by using Aprori algorithm
%A M Afshar Alam
%A Sapna Jain
%A Ranjit Biswas
%J International Journal of Innovative Technology and Creative Engineering
%D 2011
%I International Journal of Innovative Technology and Creative Engineering
%X Data Mining is concerned with the development and applications of algorithms for discovery of a priori unknown relationships associations, groupings, classifiers from data. Association rule mining (ARM) is a knowledge discovery technique used in various data mining applications. The task of discovering scalable rules from the multidimensional database with reduced support is an area for exploration for research . Pruning is a technique for simplifying and hence generalising a decision tree. Error-Based Pruning replace sub-trees with leaves .It uses decision class is the majority. In this paper we have proposed an algorithm DataAprori to generate scaled rules using the alarm technique. Network problems manifest themselves as an alarm sequence. Since network problems repeat more or less frequently, processing of alarm sequences from alarm history can be good base for creation of correlation rules that will be used in the future, when the same problem will appear. In this paper we have proposed DataAprori that induces a set of rules of the potential usage of the mathematical Apriori algorithm in fault management introducing logical inventory data in typical alarm by introducing the sequence detection processes. Experimental on real world datasets show that the proposed approach improves performance over existing approach in the form of High level-correlations (alarm sequences) which are detected in a telecommunication network.
%K Data mining
%K ABCDE architecture
%K pruning
%K Aprori technique.
%U http://ia601207.us.archive.org/34/items/IJITCE/IJITCE_nov3.pdf