PDF-Using Hierarchical Clustering to Enhance Classification Accuracy ...

Matthaios Theodorakis Andreas Vlachos Theodore Z Kalamboukis 1 Department of Computing Imperial College London UK University of Economics and Business avl tzkauebgr Abstract A new appro. 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. Basic Concepts and Algorithms. Bamshad Mobasher. DePaul University. 2. What is Clustering in Data Mining?. Cluster:. a collection of data objects that are “similar” to one another and thus can be treated collectively as one group. Rongcheng Lin. Computer Science Department. Contents. Motivation, Definition & Problem. Review of SVM. Hierarchical Classification. Path-based Approaches. Regularization-based Approaches. Motivation. Rongcheng Lin. Computer Science Department. Contents. Motivation, Definition & Problem. Review of SVM. Hierarchical Classification. Path-based Approaches. Regularization-based Approaches. Motivation. The Error Matrix. Error matrix for a 5-class classification. Overall Accuracy. . = (100 + 105 + 60 + 84 + 55) / 500 = 404 / 500 = 81%. Reference Map. . No. Pixels Classified as. . Class. . No. Pixels. Hossein. . Hamooni. , Abdullah . Mueen. University of New Mexico. Department of Computer Science. What is Phoneme?. Phonemes are . very small . units of intelligible . sound (usually less than 200 . 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 . 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 . Avdesh. Mishra, . Manisha. . Panta. , . Md. . Tamjidul. . Hoque. , Joel . Atallah. Computer Science and Biological Sciences Department, University of New Orleans. Presentation Overview. 4/10/2018. 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. 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.