PPT-More on Clustering in COSC 4335

Hierarchical Clustering DBSCAN 1 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. '(! 2,&'(,%3*3/"(4&/4335',+(-6,(7,.(! 8*9,+-31(+,*-$/,*-(%'$/4+(-3(*,:(6,$;6-+(((1=,-(.,9,'3#/,*-+($*(0#1$'(&#x& 7 ;3''3:,.(&(+$/$'&1(#&--,1*(-3(-6,(#1,9$3)+(/3*-6?( \IWObb(c t of Amplia & Co. In part Pallavi. . Arora. and . Huy. Nguyen. WiSeR. – Wireless System Research Group. Department of Computer Science. University of Houston, TX, USA. COSC 7388 Project Presentation. Agenda. Smart phone: a threat to . 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 . Already seen program statement types. :. rules: p(X) :- q(X,Y), r(. Y,z. ).. facts: likes(. brian. , . madonna. ).. queries: ?- likes(. madonna. , . brian. ).. Argument forms:. 1. logical .  . Arifur. . Sabeth. 11/30/2009. Introduction. Space debris, also known as orbital debris, space junk . First satellite was launch in 1957 and break up in . 1961.. sodium-potassium (. NaK. )is one . 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. 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. 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. 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.. 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 .