PPT-Mixtures, clustering, spatial

amp dynamic point processes and big data sets Mike West Department of Statistical Science Duke University cellular phenotypes in vaccine adjuvant studies Immune response studies. 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. . 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. Ensemble Clustering. unlabeled . data. ……. F. inal . partition. clustering algorithm 1. combine. clustering algorithm . N. ……. clustering algorithm 2. Combine multiple partitions of . given. data . Chapter 9.3 Part 1. Q: What is the dullest element? A: . Bohrium. . Key concepts:. Describe 3 properties of mixtures. Describe 4 methods of separating mixtures. Give everyday examples of mixtures. Key Vocabulary:. Mixtures may be separated by many different techniques based on differing physical and/or chemical properties. Sorting. Simply picking apart the different components. This can be easy and obvious…. 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 . Divisions of Matter. Pure Substance. Cannot be broken down and retain its properties.. Has a fixed composition.. Every sample of a pure substance has EXACTLY the same composition AND exactly the same characteristics - homogeneous. Chapter 5. Ex:. Melting. Freezing. Boiling/evaporation. Condensation. Sublimation. Dissolving. Bending. Crushing. Breaking. Chopping. Filtration. distillation. Physical change. Does not make a different substance. How can matter be classified?. Atoms . are the smallest unit of an element that maintains the properties of that element.. The most basic ingredients to . all . matter. Atoms can be combined in three majors ways:. 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 . 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. 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. Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values..