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SREC-DATAMINING - A PAPER PRESENTATION ON PARALLEL DATA...

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A PAPER PRESENTATION ON PARALLEL DATA MINING OF ASSOCIATION RULES AT AUTHORS:- V.SRAVAN KUMAR I. ANAND BABU III/IV B.TECH III/IV B.TECH INFORMATION TECHNOLOGY E-MAIL: [email protected] , [email protected]
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ABSTRACT The information age has seen most of the activities generating huge volumes of data. The explosive growth of business, scientific and government databases sizes has far outpaced our ability to interpret and digest the stored data. This has created a need for new generation tools and techniques for automated and intelligent database analysis. These tools and techniques are the subjects of the rapidly emerging field of data mining. The objective of this work is to generate association rules. Association rules are statement like '98% of faculty members that borrow books on vision and multimedia also borrow books on data structures'. We have considered the problem of discovering rules for large library databases. We attempt to establish rules between keywords of a topic in database and effectively use these rules to distribute books among the interested faculty members. We proceed to cover problem of parallel mining of association rules on distributed memory multiprocessor machine. Parallel mining of association rules represent a spectrum of trade-off between computation, communication, memory usage, synchronization and use of problem specific information. We have implemented the “Apriori" parallel algorithm. Performance measurements of parallel implementation show a good speed up behavior, compared to sequential implementation of the same algorithm. These rules can then be used to efficiently publicize books among those faculty members, who would be interested in them.
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1. Data Mining: Generally, Data Mining (sometimes called knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Information can be used to increase revenue, cuts costs or both. Data mining software use one of the best analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it and summarize the relationships identified. Technically, Data Mining is the process of finding correlations or patterns among multiple of fields in large relational databases. 1.1 Data Mining Techniques : Data mining is the component of KDD process, may involve repeated iterative application of Data Mining methods. The primary goal of Data Mining is to produce knowledge that the user can act upon. It does that by building a model of real world based on the data collected from a variety of sources, which may include corporate transactions, customer's history, customer demographic information, and relevant external database such as credit bureau information etc., the result of the model building exercise is a description of pattern and the relationships in the data.
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