This paper presents a new density-based clustering algorithm, ST-DBSCAN, which is based on DBSCAN. We propose three marginal extensions to DBSCAN related with the identification of (i) core objects, (ii) noise objects, and (iii) adjacent clusters. In contrast to the existing density-based clustering algorithms, our algorithm has the ability of discovering clusters according to non-spatial, spatial and temporal values of the objects. In this paper, we also present a spatial-temporal data warehouse system designed for storing and clustering a wide range of spatial-temporal data. We show an implementation of our algorithm by using this data warehouse and present the data mining results.

Derya Birant

Alp Kut

Data & Knowledge Engineering

数据与知识工程 (DKE) 促进了这两个相关领域之间的思想交流和互动。 DKE 覆盖了全球范围内的研究人员、设计师、经理和用户。该杂志的主要目的是识别、调查和分析设计和有效使用这些系统的基本原则。

Data and Knowledge Engineering (DKE) facilitates the exchange of ideas and interaction between the two related fields of data and knowledge. The journal encompasses researchers, designers, managers, and users from around the globe. Its primary aim is to identify, investigate, and analyze the fundamental principles for designing and effectively utilizing these systems.

《数据与知识工程》(Data and Knowledge Engineering) 促进了数据与知识这两个相关领域之间的思想交流与互动。该期刊覆盖了全球范围内的研究人员、设计师、管理者和用户。其主要目的是识别、调查和分析设计及有效使用这些系统的基本原则。

Data and Knowledge Engineering

ST-DBSCAN: An algorithm for clustering spatial–temporal data

A new density-based clustering algorithm based on DBSCAN, which has the ability of discovering clusters according to non-spatial, spatial and temporal values of the objects, is presented and an implementation of the algorithm is shown by using this data warehouse and the data mining results are presented.

Category	Quartile
计算机科学	3
计算机科学, 计算机人工智能	4
计算机科学, 计算机信息系统	4

ST-DBSCAN: An algorithm for clustering spatial–temporal data

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	2