BIG DATA MINING FOR INTERESTING PATTERNS WITH MAP REDUCE TECHNIQUE

Nikhil Jamdar; A Vijayalakshmi

doi:10.22159/ajpcr.2017.v10s1.19634

Authors

Nikhil Jamdar School of Computer Science and Engineering, VIT University, Chennai, Tamil Nadu, India
A Vijayalakshmi School of Computer Science and Engineering, VIT University, Chennai, Tamil Nadu, India

DOI:

https://doi.org/10.22159/ajpcr.2017.v10s1.19634

Keywords:

Map Reduce Model, Frequent Pattern, Constraints, Uncertain Data, Data Search and Mining

Abstract

There are many algorithms available in data mining to search interesting patterns from transactional databases of precise data. Frequent pattern mining is a technique to find the frequently occurred items in data mining. Most of the techniques used to find all the interesting patterns from a collection of precise data, where items occurred in each transaction are certainly known to the system. As well as in many real-time applications, users are interested in a tiny portion of large frequent patterns. So the proposed user constrained mining approach, will help to find frequent patterns in which user is interested. This approach will efficiently find user interested frequent patterns by applying user constraints on the collections of uncertain data. The user can specify their own interest in the form of constraints and uses the Map Reduce model to find uncertain frequent pattern that satisfy the user-specified constraints

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