Differential Evolution for Large-Scale Clustering
Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering · 2019
Clustering is the task of organizing data instances into groups based on the similarity between them.It p lays an essential role in knowledge discovery and data min ing, ranging fro m preprocessing step to the final goal o f the task.Evo lutionary algorith ms (EAs) based clustering methods have developed with the intention of enhancing the effectiveness and accurateness of clustering.The huge amount of data emerging by the progress of technology have become data clustering as a challenging task and as an attractive attention for the use of EAs based approaches.Differential Evolut ion (DE), an in stance of EAs, has been explo ited to discover the best solution for clustering problems.It has become a successful solution to produce more compact clusters than other traditional clustering techniques.This paper presents a parallel differential evolution algorith m on Spark framewo rk to facilitate huge amount of data clustering.Experimentations were conducted on some frequently used UCI mach ine learn ing datasets.The results have presented that the proposed approach is effective and comparable to existin g algorithms.