Data mining is one of the computing processes that support discovering the large data sets which involves methods. The main task of data mining is to come across unexpected, useful and interesting pattern in a large database (Neha Goyal et al., 2016). Pattern mining is a sub domain of data mining which detects the stable frequent patterns between data. Frequent patterns are nothing but a substructures or sequences which are defined by the user on transactional database that is equal or greater than a threshold. This pattern mining can be applied on various types of data such as transaction database, stream database and sequence database. Here the main aspect is concentrated on data mined from frequent pattern mining is may be certain or uncertain.
Many Frequent pattern mining algorithms for mining uncertain data from a larger database, these data of interest may be of many forms such as vectors, tables, texts, images, etc. Many frequent subgraph mining algorithms are allowed for extracting data, which are categorized into two types they are Apriori based approaches and Pattern growth based
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Discovering the frequent data on a large set are simply challenge where the search space is more exponential on number of database attributes and with billions of data objects. Here scanning the frequent items is mostly concentrated on data mining, where many researchers are providing the algorithm with innovative ideas which helps to solve the issues on retrieving the frequent data. Among association rule mining and distributed ARM algorithms (not feasible for data resident in the obtainable memory), Parallel distributed algorithm achieves feasible results than the Apriori algorithms that on such cases like higher data volumes and low minimum supports. Thus this algorithm has a higher computation time for retrieving the frequent