PPT-Hierarchical Agglomerative Clustering on graphs

Sushmita Roy sroybiostatwiscedu Computational Network Biology Biostatistics amp Medical Informatics 826 Computer Sciences 838 httpscompnetbiocoursediscoverywiscedu Nov 3 rd 2016 RECAP. k. -center clustering. Ilya Razenshteyn (MIT). Silvio . Lattanzi. (Google), Stefano . Leonardi. (. Sapienza. University of Rome) and . Vahab. . Mirrokni. (Google). k. -Center clustering. Given:. Hierarchical Clustering . 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. 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. Tugba . Koc Emrah Cem Oznur Ozkasap. Department of . Computer . Engineering, . Koç . University. , Rumeli . Feneri Yolu, Sariyer, Istanbul . 34450 Turkey. Introduction. Epidemic (gossip-based) principles: highly popular in large scale distributed systems. Oliver van . Kaick. 1,4 . . Kai . Xu. 2. . Hao. Zhang. 1. . Yanzhen. Wang. 2. . Shuyang. Sun. 1. Ariel Shamir. 3. Daniel Cohen-Or. 4. 4. Tel Aviv University. 1. Simon . Fraser University. 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”. Classification of Transposable Elements . using a Machine . Learning Approach. Introduction. Transposable Elements (TEs) or jumping genes . are DNA . sequences that . have an intrinsic . capability to move within a host genome from one genomic location . 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 ?. 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 . 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. Introduction to Data Mining, 2. nd. Edition. by. Tan, Steinbach, Karpatne, Kumar. Two Types of Clustering. Hierarchical. Partitional algorithms:. Construct various partitions and then evaluate them by some criterion. Sampath Jayarathna. Cal Poly Pomona. Hierarchical Clustering. Build a tree-based hierarchical taxonomy (. dendrogram. ) from a set of documents.. One approach: recursive application of a . partitional. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. https://compnetbiocourse.discovery.wisc.edu. Nov 1. st. 2018. Goals for today. Finding modules on graphs/Community structure on graphs/Graph clustering.