Association Rule Generation in Data Streams Using FP-Growth and APRIORI MR Algorithms
Data stream is used for handling dynamic databases in which data can be arrived continuously, limitless and its size are very large. This situation has created a problem, i.e. to perform the mining process in these database, the existing data mining algorithms are not suitable. In order to perform mining task in data streams there is a need for development of new algorithms and techniques. By using this new algorithms and techniques the authors can able to perform various data mining tasks, i.e. clustering, classification, frequent pattern mining and association rule mining in data streams.