PPT-ECE 596 HW 2 Notes 1 K-means clustering

2 Pixelwise image segmentation in RGB color space Kmeans clustering 3 1 Make a copy of your original image Kmeans clustering 4 1 Make a copy of your original image Copying input image to a buffer image. x and want to group the data into a few cohesive clusters Here as usual but no labels are given So this is an unsupervised learning problem The means clustering algorithm is as follows 1 Initialize cluster centroids 57525 57525 randomly 2 Repeat u shan@cs.unc.edu. Clustering Techniques and Applications to Image Segmentation. Roadmap. Unsupervised learning. Clustering categories. Clustering algorithms. K-means. Fuzzy c-means. Kernel-based . Graph-based. 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. 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. 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”. 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 . 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. Department of Biological Sciences. National University of Singapore. http://. www.cs.ucdavis.edu. /~. koehl. /Teaching/BL5229. koehl@cs.ucdavis.edu. Clustering is a hard problem. Many possibilities; What is best clustering ?. clusters. CS771: Introduction to Machine Learning. Nisheeth. K. -means algorithm: . recap. 2. Notation: . or . is a . -dim one-hot vector. (. = 1 and . mean the same).  . K-means loss function: recap.