PPT-Chapter 9 Finding Groups of Data – Clustering with k-means

Chapter 9 Finding Groups of Data Clustering with kmeans Objectives The ways clustering tasks differ from the classification tasks we examined previously How clustering defines a group and how such groups are identified. Large-scale Single-pass k-Means . Clustering. Large-scale . k. -Means Clustering. Goals. Cluster very large data sets. Facilitate large nearest neighbor search. Allow very large number of clusters. Achieve good quality. Machine . Learning . 10-601. , Fall . 2014. Bhavana. . Dalvi. Mishra. PhD student LTI, CMU. Slides are based . on materials . from . Prof. . Eric Xing, Prof. . . William Cohen and Prof. Andrew Ng. Machine . Learning . 10-601. , Fall . 2014. Bhavana. . Dalvi. Mishra. PhD student LTI, CMU. Slides are based . on materials . from . Prof. . Eric Xing, Prof. . . William Cohen and Prof. Andrew Ng. Brendan and Yifang . April . 21 . 2015. Pre-knowledge. We define a set A, and we find the element that minimizes the error. We can think of as a sample of . Where is the point in C closest to X. . 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 . Machine . Learning . 10-601. , Fall . 2014. Bhavana. . Dalvi. Mishra. PhD student LTI, CMU. Slides are based . on materials . from . Prof. . Eric Xing, Prof. . . William Cohen and Prof. Andrew Ng. Lecture . 6. Dr. Lev . Faivishevsky. March, 2016. Agenda. Clustering. Hierarchical. K-means. GMM. Anomaly Detection. Change Detection. 3. Clustering. Partition unlabeled examples into disjoint subsets of . David Kauchak. CS . 158. . – Fall . 2016. Administrative. Final project. Presentations on . Tuesday. 4. . minute max. 2. -. 3. slides. . . E-mail me by . 9am . on . Tuesday. What problem you tackled and results. Fuzzy . k. -means. Self-organizing maps. Evaluation of clustering results. Figures and equations from Data Clustering by . Gan. et al.. Center-based clustering. Have objective functions which define how good a solution is;. 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”. 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 . Object Oriented Data Analysis. J. S. Marron. Dept. of Statistics and Operations Research. University of North Carolina. Support Vector Machines. Motivation:. Find a linear method that . “. works well. Gettysburg College. Laura E. Brown. Michigan . Technological University. Outline. Unsupervised versus Supervised Learning. Clustering Problem. k. -Means Clustering Algorithm. Visual. Example. Worked Example. 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 .