PPT-Wine Clustering Ling Lin

Contents Motivation Data Dimension ality ReductionMDS Isomap ClusteringKmeans Ncut Ratio Cut SCC Conclustion Reference Motivation Clustering is a main task of exploratory data mining. Ex Fr Karst Chi Zhi Ganoderma japonicum Fr Lloyd Zi Zhi Pharmaceutical Name Ganoderma Properties sweet neutral Channels Entered Heart Liver Lung Ling Zhi Ganoderma hapter 14 ection 2 ourishing erbs that alm the hen pirit Copyright 2004 Chinese M Ling-6 sound m m um ah a th ee ee oo oo sh sh s s plash Cochlear has developed a pack of Listening, Speech and Language cards which contain the Ling-6 sounds. You can download these cards from ou 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 . glm. . {stats. }. . glm. is used to fit generalized linear models, specified by giving a . symbolic . description . of the linear predictor and a description of the . error . . distribution.. Contents. Motivation. Data. Dimension. ality. . Reduction-MDS, Isomap. Clustering-Kmeans, Ncut, Ratio Cut, SCC. Conclustion. Reference. Motivation. Clustering is a main task of exploratory data mining. 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 .