Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology A Novel Density-Based Clustering Framework by Using Level Set Method Presenter : Zhen-Feng Weng Authors : Xiao-Feng Wang and De-Shuang Huang 2010/02/23 TKDE.17 (2009)

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 2 Outline Motivation Objective Method Experiments Conclusion Comments

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 3 Motivation DBSCAN is very sensitive to the selection of MinPts and Eps.  The drawbacks of other approaches: The overfitting phenomenon. The confusion between cluster boundary and noises. Clusters DBSCAN Real

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 4 Objective It proposed a level set method for clustering.  Base on the assumption that the cluster centers can be regarded as the target objects (image segmentation). The cluster center: To find a local maximum of density function. The image segmentation: To find a local optimum of the intensity function.

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 5 Concept It applies Geometric Active Contour (GAC) model to obtain the boundaries of clusters.  Elastic  Evolution

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 6 Overview Nonparametric density estimation Initialization of Level Set Evolution Level Set Evolution Level Set Density Valley seeking

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 7 Kernel Density Estimation & Initial Boundaries Nonparametric density estimation: Initialization of LSE: Gaussian kernel Δf(x)>0, x is inside B Δf(x)=0, x belongs B Δf(x)<0, x is outside B

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 8 Level Set Evolution The initial boundaries can be further divided into three types:  Single-cluster, Multiple-cluster, No-cluster

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 9 Valley Seeking It is a graph-based theoretic clustering method.  Each tree represents a cluster. According to the previous step: Construct the forest:

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 10 Experiments Comparisons with DBSCAN DBSCAN Level Set Method

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 11 Performance Evaluation

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 12 Conclusions It proposed a novel density-based clustering framework using the level set method.  avoid the overfitting phenomenon.  solve the confusion problem of cluster boundary points and outliers

N.Y.U.S.T. I. M. Intelligent Database Systems Lab 13 Comments Advantage  A novel idea, overcome the overlap problem Drawback  Lack of demonstrations for overfitting Application  …