期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：87
期号：3
出版社：Journal of Theoretical and Applied
摘要：Clustering is one of the phenomenal process towards information retrieval and knowledge discovery. Cluster optimality is still a questionable factor for current benchmarking clustering strategies. In particular document clustering is most sensible towards information retrieval and knowledge discovery, which is due to the curse of high volume and high dimensionality observed in recent times. In order to this many of document clustering models have been devised in recent times, but all of these models are questionable either the case of cluster optimality, process time complexity or adoptability. Henceforth, here we devised a deep machine learning approach called incremental evolutionary genetic algorithm based optimal document clustering (ODC) process. The experiments were done on documents dataset with curse of high dimensionality and volume. The results obtained from the experiments observed to be remarkably optimistic towards document clustering and also evincing the linearity in time complexity and memory usage.
