Online frame-based clustering with unknown number of clusters. (September 2016)

• Record Type: Journal Article
• Authors: Saki, Fatemeh; Kehtarnavaz, Nasser
• Is Part Of: Pattern recognition. Volume 57(2016:Sep.)
• Page Start: 70
• Page End: 83
• Publication Date: 2016-09
• Subjects: Online clustering for streaming data -- Frame-based clustering -- Clustering with unknown number of clusters; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.03.010
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: This paper presents an online frame-based clustering algorithm (OFC) for unsupervised classification applications in which data are received in a streaming manner as time passes by with the number of clusters being unknown. This algorithm consists of a number of steps including density-based outlier removal, new cluster generation, and cluster update. It is designed for applications when data samples are received in an online manner in frames. Such frames are first passed through an outlier removal step to generate denoised frames with consistent data samples during transitions times between clusters. A classification step is then applied to find whether frames belong to any of existing clusters. When frames do not get matched to any of existing clusters and certain criteria are met, a new cluster is created in real time and in an on-the-fly manner by using support vector domain descriptors. Experiments involving four synthetic and two real datasets are conducted to show the performance of the introduced clustering algorithm in terms of cluster purity and normalized mutual information. Comparison results with similar clustering algorithms designed for streaming data are also reported exhibiting the effectiveness of the introduced online frame-based clustering algorithm. Highlights: Online frame-based clustering algorithm without having any knowledge of number of clusters. For applications when samples of a class appear in streaming frames. Superior to existing algorithms applicable to online frame-based clustering.