BIG DATA MAP REDUCING TECHNIQUE BASED APRIORI IN DISTRIBUTED MINING

Frequent pattern Mining is an important discovery in data mining tasks. Thus, it has been the subject of numerous studies and research since its concept came . Mostly studies find all the frequent patterns from collection of precise data, in which the items within each datum or transaction are definitely known. But, in many real-life scenario in which the user is interested in only some tiny portions of these frequent patterns. Thus we go for constrained mining , which aims to find only those frequent patterns that are interesting to the user. Moreover, there are also many real-life scenario in which the data are uncertain .In our project, we propose algorithms which will efficiently find frequent patterns and by applying constraint from collections of uncertain data.