Journal Article

A Clustering Algorithm for Multi-Modal Heterogeneous Big Data With Abnormal Data

Yan, An, Wang, Wei, Ren, Yi, Geng, HongWei

2021

Overview

The problems of data abnormalities and missing data are puzzling the traditional multi-modal heterogeneous big data clustering. In order to solve this issue, a multi-view heterogeneous big data clustering algorithm based on improved Kmeans clustering is established in this paper. At first, for the big data which involve heterogeneous data, based on multi view data analyzing, we propose an advanced Kmeans algorithm on the base of multi view heterogeneous system to determine the similarity detection metrics. Then, a BP neural network method is used to predict the missing attribute values, complete the missing data and restore the big data structure in heterogeneous state. Last, we ulteriorly propose a data denoising algorithm to denoise the abnormal data. Based on the above methods, we construct a framework namely BPK-means to resolve the problems of data abnormalities and missing data. Our solution approach is evaluated through rigorous performance evaluation study. Compared with the original algorithm, both theoretical verification and experimental results show that the accuracy of the proposed method is greatly improved.

• Publisher: Frontiers Research Foundation,Frontiers Media S.A
• Subject: Algorithms / Big Data / BP neural network / Clustering / Data collection / data integrity / Data mining / Deep learning / Expected values / Kmeans / missing attributes / multi-view / Neural networks / Neuroscience / noise reduction processing / Optimization