Incremental Outlier Feature Clustering Algorithm in Blockchain Networks Based on Big Data Analysis

Outlier detection in data mining aims at identifying anomalous observations known as outliers. The standard communication network incremental outlier feature clustering technique has a low clustering impact, requires several iterations, and takes a long clustering time. A novel incremental outlier based on big data analysis is proposed to overcome these problems for communicating networks in a Blockchain environment. Through big data analysis, the Point feature clustering algorithm uses the kernel density estimation technique with Gaussian kernel function to estimate the incremental outlier density of the communication network in the Blockchain framework. It mines the incremental outlier and applies the empirical mode based on the outlier mined for optimal outcome. The decomposition technique is used to extract outlier features, give weights to the features, and calculate the outlier degree and clusters. According to the results, the communication network, based on the Blockchain environment, may be clustered for incremental outlier features, and then outliers are ideally detected optimally. The simulation of the proposed technique is performed on three types of datasets. The simulation to form a cluster with the proposed method takes around 10 s of clustering time. The simulation results reveal that the proposed incremental outlier feature clustering (OFC) technique has a better outlier feature clustering impact on Blockchain-based networks, and requires a fewer iterations due to low computational and space complexity than other existing clustering techniques.