基于改进模糊聚类算法的大数据随机挖掘仿真

Simulation of Big Data Random Mining Based on Improved Fuzzy Clustering Algorithm

大数据挖掘是从大量有噪声的、随机模糊的大数据中提取有价值信息的过程,由于海量大数据具有多维性、稀疏性以及动态性等特点,准确获取其分布特征的难度较大,随机挖掘难以直接实现。为此提出基于改进模糊聚类算法的大数据随机挖掘方法。利用建立的语义概念树模型获取大数据的特征分布关系,并根据模糊语义分析法得出大数据的语义相似性、关联性条件,提取大数据特征。优先确定最佳聚类数,采用改进模糊聚类算法对其聚类,实现基于改进模糊算法的大数据随机挖掘。实验结果表明,上述方法的大数据模糊聚类效果较好,随机挖掘准确率可达到95%以上,实验所得结果验证了上述方法较强的应用有效性。

Big data mining is a process of extracting valuable information from noisy and random fuzzy big data.Due to the multi-dimensional,sparse and dynamic characteristics of massive big data,it is difficult to obtain its distribution characteristics accurately.As a result,a random mining method of big data based on an improved fuzzy clustering algorithm was presented.The semantic concept tree model was used to obtain the feature distribution of big data.Then,semantic similarity and conditions of big data were obtained by the fuzzy semantic analysis method.Meanwhile,the features of big data were extracted.Moreover,the optimal clustering number was determined and then clustered by the improved fuzzy clustering algorithm.Finally,the random mining of big data based on the improved fuzzy algorithm was achieved.Experimental results show that the fuzzy clustering effect of the proposed method is good,and the random mining accuracy can be more than 95%.Therefore,the method has strong application and effectiveness.