PDF-IFCS'061A divisive approach for clustering symbolic dataM. ChaventMath

IFCS0621 Divisive clustering methoddescendant hierarchical algorithmclassical or symbolic data2 Application for clustering a set of categoriesexample of a set of species contaminated with mercuryco. M[xi] S:requestforxiC!M[xi]!S:xi,dataM[xi+1] S:requestforxi+1C!M[xi+1]!S:xi+1,data Hereweonlyshowonedirectionofcommunication,fromtheclienttotheserver.Thereversedirectionpro-ceedsinexactlythesamewaybut Adapted from Chapter 3. Of. Lei Tang and . Huan. Liu’s . Book. Slides prepared by . Qiang. Yang, . UST, . HongKong. 1. Chapter 3, Community Detection and Mining in Social Media.  Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. . Corina. . Pasareanu. Carnegie Mellon/NASA Ames. c. orina.s.pasareanu@nasa.gov. Overview. “Classical” symbolic execution and its variants. Generalized symbolic execution . D. ynamic and . concolic. Presented By:. Loris D’Antoni . Joint work with:. Margus. . Veanes. Outline. Symbolic Automata and Transducers. Extended Symbolic Automata and Transducers. Some negative results. Some positive results. Serve as a representation of a specific person, act, deed, place or conflict. They are easily recognizable but not as common as situational archetypes.. The Archetypes Include: . Light vs. Darkness. 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 . of Ernest Bormann. 18. Symbolic Interaction Theory. . . VIRAL VIDEOS. How do videos go viral?. Symbolic Interaction Theory. Theory helps explain the relationships among. TASTEMAKERS. Cristian. . Cadar. , Patrice . Godefroid. , . Sarfraz. . Khurshid. , . Corina. . Pasareanu. , . Koushik. . Sen. , Nikolai . Tillmann. , Willem . Visser. Overview. S. ymbolic execution and its variants. 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 . 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 . Understand how adversaries try to in31uence behaviorAdversaries spread false or misleading information to blur the line between fact and 31ction Read about the tactics foreign adversaries use below so 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.