.png){kind=icon}
.png){kind=icon}
Return
Content driven clustering algorithm combining density and distance functions
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2018.10.007.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Density and distance based clustering are two distinct approaches to the same problem. In this contribution, a novel algorithm is presented in order to exploit the benefits of both approaches. This is achieved, not by combining those approaches into a single notion, but by utilizing the advantages of each one, depending on what each step of the algorithm aims to achieve. To be precise, the Window Density Function is utilized to provide regions of high density and hence a region of clusters or a part of a cluster. Affinity Propagation is, consequently, utilized to provide a group of clusters within such a region. Finally, these regions are merged to form actual clusters. The proposed methodology is tested on a variety of synthetic and real-life datasets. The algorithm presented in this contribution outperforms other well-known algorithms, with which it is compared to, in the majority of the datasets used. (C) 2018 Elsevier Ltd. All rights reserved.
Keywords:
Clustering algorithms
Density based clustering
Distance based clustering
Evolutionary clustering
Window density function
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W