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- W2904920681 abstract "Clustering aims at grouping a given set of data points into a number of clusters without resorting to any a priori knowledge. Due to its important applications in data mining, many techniques have been developed for clustering. Being one of the most popular modern clustering algorithms, spectral clustering is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. However, it is not very well scalable to modern large datasets which typically have millions of items. To partially circumvent this drawback, in this paper, we propose an integration-based fast incremental spectral clustering algorithm which is particularly designed for image segmentation tasks. The algorithm first divides a given large dataset into several smaller partitions, next applies spectrum clustering to each partition, and finally integrates them using a BIRCH tree. Experiments performed on image data demonstrate the efficacy of our method." @default.
- W2904920681 created "2018-12-22" @default.
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- W2904920681 date "2017-12-01" @default.
- W2904920681 modified "2023-09-26" @default.
- W2904920681 title "A Fast Incremental Spectral Clustering Algorithm for Image Segmentation" @default.
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- W2904920681 doi "https://doi.org/10.1109/csci.2017.68" @default.
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