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

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 53; no. 9; pp. 10559 - 10573
• Main Authors: Kashani, Elham S., Bagheri Shouraki, Saeed, Norouzi, Yaser, De Baets, Bernard
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-05-2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Clustering; Computer Science; Data points; Density; Electrical engineering; Fuzzy logic; Fuzzy sets; Machines; Manufacturing; Mechanical Engineering; Methods; Neighborhoods; Processes; Processing speed; Aggregation; dimensional data; Density-grid-based method; Clustering; Diffusion
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-022-03711-0

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.