Apriori algorithm mines the data from the large scale data warehouse using association rule mining. In this paper a new algorithm named as Dynamic Apriori (D-Apriori) algorithm is presented. The proposed D-Apriori algorithm incorporates the dynamism in classical Apriori for efficiently mining the frequent itemsets from a large scale database. With the help of experimental results, it is shown that the D-Apriori algorithm performs better than the existing Apriori algorithm with respect to execution time for the dynamic behavior of data itemset.
KDD process includes how data is stored and accessed, how andwhat algorithms can apply to large amount of data efficiently,how results can be interpreted and visualized. KDD is theprocess of identifying valid, interesting and understandablepatterns in data. In this paper we will describe conceptual threePhase Iterative Model of KDD. The main layers of this purposedmodel are: Philosophy Layer, Technique Layer and ApplicationLayer. We will also perform the comparison of Tradition KDDModel with Three Phase Iterative Model.
KDD model becomes used in financial process. Data Mining tools can be used to improve the efficiency of the professionals. The integration of Data Mining tools with the traditional financial research methods is relatively a new concept. If Data Mining tools for Financial are developed then it make the process fast, cheaper and relatively much more efficient. In this paper we have discussed the three phase model of KDD on Financial Research.
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