A density-grid-based method for clustering k-dimensional data

E. S. Kashani

doi:10.1007/s10489-022-03711-0

A density-grid-based method for clustering k-dimensional data

2022-08-20

0

PRE

AI

E

E. S. Kashani *

S

Saeed Bagheri Shouraki

DOI：10.1007/s10489-022-03711-0

Original

Original request for help

Save

Abstract

En 中文

In this paper, we propose a novel density-grid-based method for clustering k-dimensional data. KIDS, an acronym for K-dimensional Ink Drop Spread, detects densely-connected pieces of data in k-dimensional grids. It enables one to simultaneously exploit the advantages of fuzzy logic, as well as both density-based and grid-based clustering. In the proposed method, the k-dimensional data space is divided into different cells. Input data records are mapped to the cells. The data points are then spread in the k-dimensional cells, just like what happens to ink drops in water. So the cells adjacent to the data cells also represent the data. Eventually, the impacts of all data grid cells are condensed and compared with the threshold to compute the final clusters. The experimental results show that the method has superior quality and efficiency in both low and high dimensions. In addition, the method is not only robust to noise but it is also capable of finding clusters of arbitrary shapes.

Keywords:

Clustering

Density-grid-based method

k-dimensional data

Diffusion

Aggregation

A density-grid-based method for clustering k-dimensional data

Abstract

Journal

Organization