From Partition-Based Clustering to Density-Based Clustering: Fast Find Clusters With Diverse Shapes and Densities in Spatial Databases

• Published in: IEEE access Vol. 6; pp. 1718 - 1729
• Main Authors: Wang, Jiang, Zhu, Cheng, Zhou, Yun, Zhu, Xianqiang, Wang, Yilin, Zhang, Weiming
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Clustering; Clustering algorithms; Clustering methods; Datasets; Density; diverse shapes and densities; efficiency on large spatial databases; partition-and-merge strategy; Partitioning algorithms; Partitions; Shape; Spatial clustering; Spatial data; Spatial databases; Time complexity
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2780109

Spatial data clustering has played an important role in the knowledge discovery in spatial databases. However, due to the increasing volume and diversity of data, conventional spatial clustering methods are inefficient even on moderately large data sets, and usually fail to discover clusters with diverse shapes and densities. To address these challenges, we propose a two-phase clustering method named KMDD (clustering by combining K-means with density and distance-based method) to fast find clusters with diverse shapes and densities in spatial databases. In the first phase, KMDD uses a partition-based algorithm (K-means) to cluster the data set into several relatively small spherical or ball-shaped subclusters. After that, each subcluster is given a local density; to merge subclusters, KMDD utilizes the idea that genuine cluster cores are characterized by a higher density than their neighbor subclusters and by a relatively large distance from subclusters with higher densities. Extensive experiments on both synthetic and real-world data sets demonstrate that the proposed algorithm has a near-linear time complexity with respect to the data set size and dimension, and has the capability to find clusters with diverse shapes and densities.