Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Dynamically evolving clustering for data streams
AU - Dutta Baruah, Rashmi
AU - Angelov, Plamen
AU - Baruah, Diganta
PY - 2014/6/2
Y1 - 2014/6/2
N2 - In this paper, a new online evolving clustering approach for streaming data is proposed, named Dynamically Evolving Clustering method. The clustering approach attempts to meet the following three key requirements of data streamclustering: (i) fast and memory efficient (ii) adaptive (iii) robust to noise. The proposed clustering approach processes one sample at a time and makes necessary changes to the model and then forgets the processed sample. This feature naturally makes it adaptive to changes in the data pattern. The clustering method considers both distance and weight before generating new clusters. This avoids generation of large number of clusters.Further, to capture the dynamics of the data stream, the weight uses an exponential decay model. Since in data streaming environment, a low density cluster can be outlier points or seed of actual cluster, DEC applies a strategy that enables detecting and removing only those low density clusters that are real outliers. To evaluate the performance of the proposed clustering approach,experiments were conducted using benchmark dataset. The results show that the Dynamically Evolving Clustering approach can separate the data well which are evolving in nature.
AB - In this paper, a new online evolving clustering approach for streaming data is proposed, named Dynamically Evolving Clustering method. The clustering approach attempts to meet the following three key requirements of data streamclustering: (i) fast and memory efficient (ii) adaptive (iii) robust to noise. The proposed clustering approach processes one sample at a time and makes necessary changes to the model and then forgets the processed sample. This feature naturally makes it adaptive to changes in the data pattern. The clustering method considers both distance and weight before generating new clusters. This avoids generation of large number of clusters.Further, to capture the dynamics of the data stream, the weight uses an exponential decay model. Since in data streaming environment, a low density cluster can be outlier points or seed of actual cluster, DEC applies a strategy that enables detecting and removing only those low density clusters that are real outliers. To evaluate the performance of the proposed clustering approach,experiments were conducted using benchmark dataset. The results show that the Dynamically Evolving Clustering approach can separate the data well which are evolving in nature.
U2 - 10.1109/EAIS.2014.6867473
DO - 10.1109/EAIS.2014.6867473
M3 - Conference contribution/Paper
SN - 9781479933471
SP - 1
EP - 6
BT - Proceedings 2014 IEEE Symposium on Evolving and Intelligent Systems, EAIS2014
PB - IEEE Xplore
T2 - 2014
Y2 - 2 June 2014 through 4 June 2014
ER -