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An extended affinity propagation clustering method based on different data density types.

Zhao X, Xu W - Comput Intell Neurosci (2015)

Bottom Line: But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously.There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type.The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Rail Traffic Control and Safety, Beijing 100044, China ; Business School, Qilu University of Technology, Jinan 250353, China.

ABSTRACT
Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

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The reachability-plot of the regional seismic data.
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Related In: Results  -  Collection


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fig13: The reachability-plot of the regional seismic data.

Mentions: The experiment result is shown in Figure 12. The seismic data are grouped into three clusters: red, green, and blue. Then we apply the OPTICS algorithm to obtain a reachability-plot of the seismic data set and an estimated threshold for the classification which is shown in Figure 13. There are four grouped clusters when Eps1* = 3.5 × 104 (m), as shown in Figure 14. This result has two different aspects from our method: firstly, there appears a brown cluster; secondly, the red, green, and blue clusters are held in common by two algorithms that contain more earthquakes in OPTICS than in our algorithm.


An extended affinity propagation clustering method based on different data density types.

Zhao X, Xu W - Comput Intell Neurosci (2015)

The reachability-plot of the regional seismic data.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4317584&req=5

fig13: The reachability-plot of the regional seismic data.
Mentions: The experiment result is shown in Figure 12. The seismic data are grouped into three clusters: red, green, and blue. Then we apply the OPTICS algorithm to obtain a reachability-plot of the seismic data set and an estimated threshold for the classification which is shown in Figure 13. There are four grouped clusters when Eps1* = 3.5 × 104 (m), as shown in Figure 14. This result has two different aspects from our method: firstly, there appears a brown cluster; secondly, the red, green, and blue clusters are held in common by two algorithms that contain more earthquakes in OPTICS than in our algorithm.

Bottom Line: But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously.There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type.The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Rail Traffic Control and Safety, Beijing 100044, China ; Business School, Qilu University of Technology, Jinan 250353, China.

ABSTRACT
Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

Show MeSH