PPT-k -Means Clustering Todd W. Neller

Gettysburg College Laura E Brown Michigan Technological University Outline Unsupervised versus Supervised Learning Clustering Problem k Means Clustering Algorithm Visual Example Worked Example. 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. Divide the Estate. Problem 8.10. Bargaining over 100 pounds of gold. Round 1: Todd makes offer of Division.. Steven accepts or rejects.. Round 2: If Steven rejects, estate is reduced to 100d pounds. Steven makes a new offer and Todd accepts or rejects.. 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. E. nglish . Early life . William Todd English was born August 29, 1960 in Amarillo, Texas,. He went to Guilford College in North Carolina on a baseball scholarship, . but quit and entered the Culinary Institute of America in 1978 and graduated in 1982.[3][4]. 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;. 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. Chapter 9 Finding Groups of Data – Clustering with k-means Objectives The ways clustering tasks differ from the classification tasks we examined previously How clustering defines a group, and how such groups are identified 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. DrGothelfwww.orthosports.com.au 4749Concord2931Hurstville1aAvenue,160 Dr Todd GothelfShoulder, Foot & Ankle Surgery ForefootPainToddKGothelfFootAnkleShoulder Dr Todd GothelfShoulder, Foot & Ankle Surg