Big Data Analytics in Mining Frequent Patterns from uncertain Data with Mapreduce


  • Bharti Assistant Professor, Computer Science Engineering, DCRUST, Murthal, Sonipat


Big Data, Hadoop, Data Mining.


Frequent pattern mining is a good approach to get correlation in dataset. The foremost well-liked data mining Apriori algorithm that mines frequent item set has downside that computation time will increase once data size will increase. Recently, there incorporates a fast development of internet and as fast growing cluster users, several corporations have to manage higher amount of data every day. Acquiring important information quickly from this continuously growing data is vital issue. In this paper, some of the important algorithms used in mining frequent patterns from uncertain data have been studied. Uncertainty in data is caused by factors like data randomness, data incompleteness, etc. In some circumstances, users are interested in only some of the frequent patterns instead of all. The user can express his interest in terms of constraints and push them into the mining process as a result, the search space is reduced which is termed as constrained mining. Finally, big data has brought tools for the problem of frequent pattern mining of uncertain data.


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How to Cite

Bharti. (2021). Big Data Analytics in Mining Frequent Patterns from uncertain Data with Mapreduce. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 10(1), 17–21. Retrieved from