Density Peak Clustering Algorithm Based on Shared Neighbors and Natural Neighbors and Analysis of Electricity Consumption Patterns

Abstract

The Density Peaks Clustering (DPC) algorithm is well-known for its simplicity and efficiency in clustering data of arbitrary shapes. However, it faces challenges such as inconsistent local density definitions and sample assignment errors. This paper introduces the Shared Neighbors and Natural Neighbors Density Peaks Clustering (SN-DPC) algorithm to address these issues. SN-DPC redefines local density by incorporating weighted shared neighbors, which enhances the density contribution from distant samples and provides a better representation of the data distribution. It also establishes a new similarity measure between samples using shared and natural neighbors, which increases intra-cluster similarity and reduces assignment errors, thereby improving clustering performance. Compared with DPC-CE, IDPC-FA, DPCSA, FNDPC, and traditional DPC, SN-DPC demonstrated superior effectiveness on both synthetic and real datasets. When applied to the analysis of electricity consumption patterns, it more accurately identified load consumption patterns and usage habits.