Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : K Rajeswari | A Loganayakiunnamed

Volume 03 Issue 12 2016

ISSN no:  2348-3121

Page no: 49-55

Abstract – Association Rule or Affinity Analysis is the fundamental data mining analysis to find the co-occurrence relationships like purchase behavior of customers. The analysis is legacy in sequential computation so that many data mining applications of big data shows that to mine medical relevant data in such databases, whereas the data cannot be taken already with existing history. SQL MapReduce framework as a product called Aster, it provides nPath SQL to process big data stored in the DB. Market Basket Analysis is executed on the framework but it is based on its SQL Databases with MapReduce Database. Association rule has been used efficiently to manage mine the Medical relevant data such as stock items and products etc analyzing the patient (customer) behavior. It is based on Apriority Property where all subsets of a frequent item set must also be frequent. The map() and reduce() functions run on distributed nodes in parallel. Each map and reduce operation can be processed independently on each node and all the operations can be performed in parallel. Map/Reduce can handle Big Data sets as data are distributed on HDFS, in here the minimum support basis documents are searched initially according to its relevant support co-ordinates. In our Proposed the Apriori-like algorithms for Spatio-Temporal Pattern Queries presents a way to construct Apriori-like algorithms for mining spatio-temporal patterns. This Thesis addresses problems of the different types of comparing functions that can be used to mine frequent patterns. Map-Reduce for Medical Data Mining on Multi core discuss ways to develop a broadly applicable programming paradigm that is applicable to different learning algorithms.  

Keywords— Association Rule Mining, Big Data Computing, Map Reduce, HDFS, Spatio-Temporal Patterns


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