Mining the High Dimensional Biological Dataset Using Optimized Colossal Pattern with Dimensionality Reduction

T. Sreenivasula Reddy, R. Sathya, Mallikhanjuna Rao Nuka
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Abstract

Recent years have seen a lot of attention paid to the mining of enormous item sets from high-dimensional databases. Small and mid-sized data sets take a long time to mine with traditional algorithms since they don’t include the complete and relevant info needed for decision making. Many applications, particularly in bioinformatics, benefit greatly from the extraction of (FCCI) Frequent Colossal Closed Itemsets from a large dataset. In order to extract FCCI from a dataset, present preprocessing strategies fail to remove all extraneous characteristics and rows from the data set completely. In addition, the most current algorithms for this kind are sequential and computationally expensive. A high-dimensional dataset is pruned of all extraneous characteristics and rows using two alternative dimensionality reduction strategies presented in this paper. Then, an optimal feature value is identified by using Equilibrium Optimizer (EO) to identify the threshold value for reduced features. It is designed to discover common items and build association rules if the feature value is smaller than the frequency mining algorithm (IFRS) in conjunction with the Fruit fly Algorithm (FFA). If the feature value exceeds the optimal threshold, then optimized Length restrictions can be used to solve the CP mining problem (LC). Random search is utilized to identify the optimal threshold values of the restrictions and extract the enormous pattern using the Differential Evolutionary Arithmetic Optimization Algorithm. The experiments are carried on twenty biological datasets that us extracted from UCI websites and validated the proposed models in terms of various metrics.
利用优化的巨像模式和降维技术挖掘高维生物数据集
近年来,从高维数据库中挖掘庞大的项目集受到了广泛关注。由于中小型数据集不包含决策所需的完整相关信息,因此使用传统算法挖掘这些数据集需要很长时间。许多应用,尤其是生物信息学应用,都能从大型数据集中提取 (FCCI) Frequent Colossal Closed Itemsets,并从中受益匪浅。为了从数据集中提取 FCCI,目前的预处理策略无法完全去除数据集中所有无关的特征和行。此外,目前大多数此类算法都是顺序算法,计算成本高昂。本文提出了两种可供选择的降维策略,以去除高维数据集中所有无关的特征和行。然后,通过使用均衡优化器(EO)来确定缩减特征的阈值,从而确定最佳特征值。如果特征值小于结合果蝇算法(FFA)的频率挖掘算法(IFRS),它就能发现共同项并建立关联规则。如果特征值超过最佳阈值,则可以使用优化的长度限制来解决 CP 挖掘问题(LC)。利用随机搜索来确定限制的最佳阈值,并使用差分进化算法优化算法来提取巨大的模式。实验在从 UCI 网站提取的 20 个生物数据集上进行,并通过各种指标验证了所提出的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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