A Shared Natural Neighbors Based-Hierarchical Clustering Algorithm for Discovering Arbitrary-Shaped Clusters

In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.

A novel method for discovering spatio-temporal clusters of different sizes, shapes, and densities in the presence of noise

The discovery of spatio-temporal clusters in complex spatio-temporal data-sets has been a challenging issue in the domain of spatio-temporal data mining and knowledge discovery.In this paper,a novel spatio-temporal clustering method based on spatio-temporal shared nearest neighbors(STSNN)is proposed to detect spatio-temporal clusters of different sizes,shapes,and densities in spatiotemporal databases with a large amount of noise.The concepts of windowed distance and shared nearest neighbor are utilized to define a novel spatiotemporal density for a spatio-temporal entity with definite mathematical meanings.Then,the density-based clustering strategy is employed to uncover spatio-temporal clusters.The spatio-temporal clustering algorithm developed in this paper is easily implemented and less sensitive to density variation among spatio-temporal entities.Experiments are undertaken on several simulated datasets to demonstrate the effectiveness and advantage of the STSNN algorithm.Also,the real-world applications on two seismic databases show that the STSNN algorithm has the ability to uncover foreshocks and aftershocks effectively.

DBSTC:an effective method for discovering cluster features with different spatiotemporal densities

Spatiotemporal clustering is one of the most advanced research topics in geospatial data mining.It has been challenging to discover cluster features with different spatiotemporal densities in geographic information data set.This paper presents an effective density-based spatiotemporal clustering algorithm(DBSTC).First,we propose a method to measure the degree of similarity of a core point to the geometric center of its spatiotemporal reachable neighborhood,which can effectively solve the isolated noise point misclassification problem that exists in the shared nearest neighbor methods.Second,we propose an ordered reachable time window distribution algorithm to calculate the reachable time window for each spatiotemporal point in the data set to solve the problem of different clusters with different temporal densities.The effectiveness and advantages of the DBSTC algorithm are demonstrated in several simulated data sets.In addition,practical applications to seismic data sets demonstrate the capability of the DBSTC algorithm to uncover clusters of foreshocks and aftershocks and help to improve the understanding of the underlying mechanisms of dynamic spatiotemporal processes in digital earth.