Fast and Efficient Mining of Frequent and Maximal Periodic Patterns in Spatiotemporal Databases for Shifted Instances
Mining frequent and maximal Periodic Patterns is the challenging task for the data scientists due to unstructured, dynamic and huge raw data generated from web. Big Data is a latest tendency used to examine the datasets from large, complex databases. Developers cannot supervise them through conventional algorithms or Knowledge discovery software tools. Big Data mining is the ability to extract valuable information either from these huge datasets or else streams of data, due to its three V's (Volume, Velocity, and Variety) The previous studies focus on how to find frequent patterns from large traffic and sensor data based applications. However, mining maximal and useful periodic patterns from spatiotemporal datasets is still an open research problem in weather conditions, Fraud recognition and forest fire prevention Applications. We propose two algorithms such as Enhanced Tree based pattern mining algorithm (ETMA) and Extended Frequent Pattern Mining algorithm (EFPMA, which generates all frequent and maximal periodic patterns from spatiotemporal databases. A novel framework is introduced to mine spatiotemporal patterns from Big Data. All Existing algorithms are fit in calculation of essential patterns, but more tedious if they apply for Big Data. The proposed framework and algorithms produce better results than UDDAG, STNR algorithms towards scalability and decreased run time compared to DBSCAN, ECLAT algorithms.
2016 IEEE 6th International Conference on Advanced Computing (IACC)