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APSCAN: A parameter free algorithm for clustering

APSCAN: A parameter free algorithm for clustering. Presenter : Cheng- Hui Chen Author : Xiaoming Chen, Wanquan Liu, Huining Qiu , Jianhuang Lai PRL 2011. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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APSCAN: A parameter free algorithm for clustering

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  1. APSCAN: A parameter free algorithm for clustering Presenter: Cheng-Hui Chen Author: Xiaoming Chen, Wanquan Liu, HuiningQiu, Jianhuang Lai PRL 2011

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • There are two distinct drawbacks for DBSCAN: • It has two parameters are difficult to be determined. • DBSCAN does not perform well to datasets with varying densities

  4. Objectives • This paper propose a novel parameter free clustering algorithm named as APSCAN • As a parameter free clustering method, APSCAN is different from AP and DBSCAN. It is not only suitable for a single density data like DBSCAN but also can be used to cluster density varying datasets.

  5. Methodology

  6. Methodology MinPts = 2 Noise Class ID DBSCAN algorithm

  7. Methodology Responsibility Availabilities Candidate k Candidate examplar k Supporting data point I’ Competing Candidate k’ r(i, k) a(i, k) Data point i Affinity propagation clustering algorithm

  8. APSCAN Normalized density list generation

  9. APSCAN • The Double-Density Based SCAN (DDBCAN)

  10. Synthesize the result by Label Update Rule • If all the points in Cjof Resultj are noise points in Resulti, we give a new class label for all points in cluster Cjin the updated clustering result. j • If p is in the cluster Cjin Resultjand p is in the cluster Ci in Resulti, then mark p with label Ci in the updated clustering result. • If p belongs to Cj in Resultj and p is a noise point in Resulti, but not all the points in Cj are noise points in Resulti , we give p a label as j j i i If p is a noise point both in Resulti and Resultj , it is labeled as a noise point in the updated clustering result.

  11. Experiments

  12. Experiments Dataset Three Dataset One Dataset Two

  13. Experiments Toy dataset

  14. Experiments

  15. Conclusions • In this paper can conclude that the proposed APS-CAN has the following three advantages: • It is a parameter free clustering method. • Itis suitable for clustering datasets with varying densities. • Itcan preserve the irregular structure of a dataset.

  16. Comments • Advantages • It has achieved satisfactory performance on clustering datasets with varying densities. • Applications • Clustering

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