Gravitational Search-Based Effective Knowledge Discovery Process for Spatio-Temporal Databases
Rainu Nandal, Rahul Rishi · International journal of intelligent engineering and systems · 2016
The rapidly rising and widespread use of database technology -including heterogeneous, geo-referenced and multidimensional data -there is a growing interest in developing new techniques for extracting knowledge from data.These techniques are the subject of the emerging field of Knowledge Discovery in Databases (KDD).In this paper, an algorithm to mine the intelligence rules from the input data using gravitational search (GS) algorithm is proposed.Initially, the significant patterns are selected from the input database with the use of measures like density ratio, sequence index, and density index.Subsequently, R-tree is modelled from the reduced dataset and is solved using the recently developed optimization algorithm.The rules obtained from the GS algorithm are useful for knowledge discovery.The experimentation is carried using the datasets to evaluate the performance in terms of mined rules and the computation time.The Performance metrics employed are a number of optimized rules, computation time and memory usage.From the analysis, it can be seen that the number of optimized rules decrease with increase in threshold and support.