3 years ago

Density core-based clustering algorithm with dynamic scanning radius

Clustering analysis has been widely used in many fields such as image segmentation, pattern recognition, data analysis, market researches and so on. However, the distribution patterns of clusters are natural and complex in many research areas. In other words, most real data sets are non-spherical or non-elliptical clusters. For example, face images and hand-writing digital images are distributed in manifolds and some biological data sets are distributed in hyper-rectangles. Therefore, it is a great challenge to detect clusters of arbitrary shapes in multi-density datasets. Most of previous clustering algorithms cannot be applied to complex patterns with large variations in density because they only find hyper-elliptical and hyper-spherical clusters through centroid-based clustering approaches or fixed global parameters. This paper presents DCNaN, a clustering algorithm based on the density core and the natural neighbor to recognize complex patterns with large variations in density. Density cores can roughly retain the shape of clusters and natural neighbors are introduced to find dynamic scanning radiuses rather than fixed global parameters. The results of our experiments show that compared to state-of-the-art clustering techniques, our algorithm achieves better clustering quality, accuracy and efficiency, especially in recognizing extremely complex patterns with large variations in density.

Publisher URL: www.sciencedirect.com/science

DOI: S0950705117305579

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.