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 . Ensemble Clustering. unlabeled . data. ……. F. inal . partition. clustering algorithm 1. combine. clustering algorithm . N. ……. clustering algorithm 2. Combine multiple partitions of . given. data . April 22, 2010. Last Time. GMM Model Adaptation. MAP (Maximum A Posteriori). MLLR (Maximum Likelihood Linear Regression). UMB-. MAP. for speaker recognition. Today. Graph Based Clustering. Minimum Cut. Dr. Paul Rockett . Casualty Actuaries in Europe, 31 May . 2013. 31 May 2013. Page . 1. Agenda. Introduction. Validation tools. Validating catastrophe models. The bigger picture. Summary. Challenges in Validating Catastrophe Models. 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. This conference talk outline is a starting point, not a rigid template. . Most . good speakers average two minutes per slide (not counting title and outline . slides). Most speakers use . about a dozen slides for a twenty minute presentation. . to . LC-MS Data Analysis.  . October 7 2013. . IEEE . International Conference on Big Data 2013 (IEEE . BigData. 2013. ). Santa Clara CA. Geoffrey Fox, D. R. Mani, . Saumyadipta. . Pyne. gcf@indiana.edu. 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. 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. 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 . What is clustering?. Grouping set of documents into subsets or clusters.. The Goal of clustering algorithm is:. To create clusters that are coherent internally, but clearly different from each other.