Distributed Approach for Efficiently Extracting Common Patterns from Large Datasets

In recent years, advancements in database knowledge discovery have enabled it to find important and practical information with great force. Frequent pattern mining and association rule mining has been thoroughly investigated for a great range of real-world applications. Traditional mining algorithms use memory-resident, centralised data. The performance is not effective enough when applying these methods to remote databases because of the large amount of data, bandwidth limitation, and energy limitations, especially in this era of big data. Datamining in dispersed contexts has therefore become a significant field of study.