PPT-Clustering V Outline Validating clustering results

Randomization tests Cluster Validity All clustering algorithms provided with a set of points output a clustering How to evaluate the goodness of the resulting clusters Tricky because . : Distributed Co-clustering with Map-Reduce. S. Papadimitriou, J. Sun. IBM T.J. Watson Research Center. Speaker:. 0356169. 吳宏君. 0350741. . 陳威遠. 0356042 . 洪浩哲. Outline. Introduction. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . Frank Lin. 10-710 Structured Prediction. School of Computer Science. Carnegie Mellon . University. 2011-11-28. Talk Outline. Clustering. Spectral Clustering. Power Iteration Clustering (PIC). PIC with Path Folding. Javad. . Azimi. , Paul Cull, . Xiaoli. Fern. {. azimi,pc,xfern. }@. eecs.oregonstate.edu. Oregon State University. Presenting by: Paul Cull. 1. Outline. Clustering Ensembles. Ant Clustering . Proposed Method. issue in . computing a representative simplicial complex. . Mapper does . not place any conditions on the clustering . algorithm. Thus . any domain-specific clustering algorithm can . be used.. We . What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. Unsupervised . learning. Seeks to organize data . into . “reasonable” . groups. Often based . on some similarity (or distance) measure defined over data . elements. Quantitative characterization may include. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values.. AMB Review 11/2010. Consensus Clustering . (. Monti. et al. 2002). Internal validation method for clustering algorithms.. Stability based technique.. Can be used to compare algorithms or for estimating the number of clusters in the data.. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects .