Mining favorable facets

1APSCAN: A parameter free algorithm for clusteringPresenter : Cheng-Hui ChenAuthor : Xiaoming Chen, Wanquan Liu, Huining Qiu, Jianhuang Lai

PRL 2011

Intelligent Database Systems LabNational Yunlin University of Science and TechnologyIntelligent Database Systems LabN.Y.U.S.T.I. M.12OutlinesMotivationObjectivesMethodologyExperimentsConclusionsCommentsIntelligent Database Systems LabN.Y.U.S.T.I. M.2MotivationThere 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

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Intelligent Database Systems LabN.Y.U.S.T.I. M.3ObjectivesThis paper propose a novel parameter free clustering algorithm named as APSCANAs 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. 4Intelligent Database Systems LabN.Y.U.S.T.I. M.Methodology5

Intelligent Database Systems LabN.Y.U.S.T.I. M.MethodologyDBSCAN algorithm6

Class IDNoiseMinPts = 2Intelligent Database Systems LabN.Y.U.S.T.I. M.MethodologyAffinity propagation clustering algorithm

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Data point iResponsibility Candidate kCompeting Candidate kCandidate examplar kSupporting data point IAvailabilities r(i, k)a(i, k)Intelligent Database Systems LabN.Y.U.S.T.I. M.APSCANNormalized density list generation

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Intelligent Database Systems LabN.Y.U.S.T.I. M.APSCANThe Double-Density Based SCAN (DDBCAN)

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Intelligent Database Systems LabN.Y.U.S.T.I. M.Synthesize the result by Label Update RuleIf p is a noise point both in Resulti and Resultj , it is labeled as a noise point in the updated clustering result.10ij If p is in the cluster Cj in Resultj and p is in the cluster Ci in Resulti , then mark p with label Ci in the updated clustering result.ijIf all the points in Cj of Resultj are noise points in Resulti, we give a new class label for all points in cluster Cj 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 asj

Intelligent Database Systems LabN.Y.U.S.T.I. M.10Experiments11

Intelligent Database Systems LabN.Y.U.S.T.I. M.Experiments12

Dataset OneDataset TwoDataset Three

Intelligent Database Systems LabN.Y.U.S.T.I. M.12Experiments13

Toy dataset

Intelligent Database Systems LabN.Y.U.S.T.I. M.13Experiments14

Intelligent Database Systems LabN.Y.U.S.T.I. M.ConclusionsIn this paper can conclude that the proposed APS-CAN has the following three advantages: It is a parameter free clustering method. It is suitable for clustering datasets with varying densities.It can preserve the irregular structure of a dataset.15Intelligent Database Systems LabN.Y.U.S.T.I. M.CommentsAdvantagesIt has achieved satisfactory performance on clustering datasets with varying densities.ApplicationsClustering16Intelligent Database Systems LabN.Y.U.S.T.I. M.16