Knowledge Discovery for Mining Patterns in Spatio-Temporal Databases

Knowledge discovery in spatio-temporal databases demands the development of challenging techniques to mine patterns that take into account the semantics and structural aspects of large databases (terabytes of data). Our investigation provides a new methodology to mine temporal patterns using FP (Frequent Pattern) growth algorithm that detects frequent pattern among various attributes of such databases. We have developed and implemented our method over different spatio-temporal datasets and achieved encouraging results which have been discussed in the study.