• Chinnapaga Ravi
• . M Bal Raju
• . N Subhash Chandra

Data Mining aims to search for implicit, previously known and potentially useful information from data. Big Data Mining is the capability of extracting useful information from large datasets or stream of data. The existing system attempts to search the pattern of interest from probabilistic database. However, the output sometimes includes the uncertain data from existential probabilities. In many real-life applications, users may look for a tiny portion of this large search space for Big Data Mining. The proposed system reduces the search space to a greater extent as it concentrates more on the constraints by using the Map Reduce model. The users are given complete freedom to express their interests by specifying their own constraints. Besides classification and clustering, anomaly detection, frequent pattern mining and association rule mining are included as the latter two analyze valuable data and helps the producer by finding the interesting or popular patterns that reveal customer purchase behavior. The algorithm proposed here greatly reduces the search space for Big Data mining of uncertain data, returning only those patterns that are interesting to the users for Big Data analytics

Big data models, Big data analytics, Frequent Patterns, Constraints, Uncertain Data

"AN EFFICEINT APPROACH FOR MINING ASSOCIATION RULES FROM HETROGENOUS UNCERTAIN DATA STREAMS USING BIG DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.765-769, November-2018, Available :http://www.jetir.org/papers/JETIR1811400.pdf

"AN EFFICEINT APPROACH FOR MINING ASSOCIATION RULES FROM HETROGENOUS UNCERTAIN DATA STREAMS USING BIG DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp765-769, November-2018, Available at : http://www.jetir.org/papers/JETIR1811400.pdf