Research on data stream clustering algorithms

Ding, Shifei; Wu, Fulin; Qian, Jun; Jia, Hongjie; Jin, Fengxiang

doi:10.1007/s10462-013-9398-7

Cited by 61 publications

(19 citation statements)

References 29 publications

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“…Traditional clustering algorithms, such as K-means, FCM and EM, are simple and easy to implement, but they lack the ability to process complex data. When the sample space is non-convex, the algorithm tends to fall into the local optimum [2][3][4][5]. Spectral clustering has attracted more and more attention in the academia field due to its easy-toimplement and excellent performance [6,7].…”

Section: Introductionmentioning

confidence: 99%

Image segmentation algorithm based on superpixel clustering

Lin

Ding

Wang

et al. 2018

IET Image Processing

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Section: Introductionmentioning

confidence: 99%

Image segmentation algorithm based on superpixel clustering

Lin

Ding

Wang

et al. 2018

IET Image Processing

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“…Data stream, however, is rather different. In data stream, the data is consecutive, ever-changing in a flow way [2,3]. Realtime, continuous, ordered sequences are common words used to describe the data stream.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Density Data Stream Clustering Algorithm

et al. 2015

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Now we are in the age of big data. Huge amount of data and information are generated every time. Traditional data stream algorithms are suit for the data streams with low dimension and simple structure. However, with the development of information technology, the produced data streams are becoming more and more complicated. It is particularly important to study how to find new associations and patterns from complex data to achieve the cognition ability and judgment ability like human brain. Clustering data streams with mixed attributes of irregular distribution is a big challenge in data mining. To solve this problem, we present an adaptive density data stream clustering algorithm-ADStream. ADStream is based on the online-off-line clustering framework. It can automatically recognize the initial clusters by passing messages between data points. Then a novel time-decay density clustering strategy is designed to group and update the continuously arriving data streams. Comprehensive experimental results demonstrate that ADStream is adaptive to the evolving data streams and may generate highquality clusters with fast processing rate.

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“…It also becomes one of the hot issues in the area of data mining, how to dig out the information is of interest. Clustering analysis is an important technology for data mining, and many researchers pay their attention to the clustering of stream data [1].…”

Section: Introductionmentioning

confidence: 99%