Multidimensional Association Rule Generation on sequence database using Hybrid Approach

Authors: Swapnil Bari, Pawan Parmar

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Abstract

In data mining, Association rule mining becomes one of the important tasks of descriptive technique which can be defined as discovering meaningful patterns from large collection of data. Mining frequent item set is very fundamental part of association rule mining .Hybrid dimension association rules mining algorithm satisfies the definite condition on the basis of multidimensional transaction database. Boolean Matrix based approach has been employed to generate frequent item sets in multidimensional transaction databases. When using this algorithm first time, it scans the database once and will generate the association rules. Apriori property is used in algorithm to prune the item sets. It is not necessary to scan the database again .we proposed hybrid algorithm for generation hybrid dimension association rules on sequence dataset.

Introduction

Data mining is the main part of KDD. Data mining normally involves four classes of task; classification, clustering, regression, and association rule learning Data mining as a field of study involves the integration of ideas from many domains rather than a pure discipline the four main disciplines [1], which are contributing to data mining include: • Statistics: it can make available tools for measuring importance of the given data • • estimating probabilities and many other tasks (e. g. linear regression). • Machine learning: it provides algorithms for inducing knowledge from given data (e g. SVM). • Data management and databases: in view of the fact that data mining deals with huge size of data, an efficient way of accessing and maintaining data is needed. • Artificial intelligence: it contributes to tasks involving knowledge encoding or search techniques (e. g. neural networks).

Conclusion

This paper presenting an algorithm for generating hybrid dimensional association rules mining as a generalization of inter-dimension and Intradimensional rule. The algorithm is based on the concept that the larger number of values/categories in a dimension/attribute means the lower degree of association among the items in the transaction. Moreover, to generalize inter-dimension association and intra-dimensional rules. we measured the following factors for creating our new idea, which are the time and the no of iteration, these factors, are affected by the approach for finding the frequent itemsets. Work has been done to develop an algorithm, which is an improvement over Apriori with using an approach of improved Apriori algorithm for a transactional database.