Estimation of Evolutionary Optimization Algorithm for Association Rule using Spatial Data Mining

N. NagaSaranya, M. Hemalatha
<span title="2012-08-30">2012</span> <i title="Foundation of Computer Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b637noqf3vhmhjevdfk3h5pdsu" style="color: black;">International Journal of Computer Applications</a> </i> &nbsp;
The innovative process for spatial data is more risk when compared to relational data. This can be functional for the efficiency and effectiveness of algorithms as well as the difficulty of possible patterns that can be establish in a spatial database. To optimize the rules generated by Association Rule Mining (Apriori method) [1] use hybrid evolutionary algorithm. This research paper present a novel hybrid evolutionary algorithm (HEA) [2] which uses particle swarm optimization for spatial
more &raquo; ... iation rule mining with clustering. The proposed HEA algorithm is to enhance the performance of Multi objective genetic algorithm [3] [4] by incorporating local search, particle swarm optimization (PSO), for Multi objective association rule mining. Thereafter, particle swarm is performed to come out of local optima. From the experiment results, it is shown that the proposed HEA algorithm has superior performance when compared to other existing algorithms.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/8019-8204">doi:10.5120/8019-8204</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tir6kxj7xbhzpdcsalwh326o6y">fatcat:tir6kxj7xbhzpdcsalwh326o6y</a> </span>
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