PPT-Parallel Deterministic Annealing Clustering and its Application

to LCMS Data Analysis   October 7 2013 IEEE International Conference on Big Data 2013 IEEE BigData 2013 Santa Clara CA Geoffrey Fox D R Mani Saumyadipta Pyne gcfindianaedu. Jong Youl Choi, Judy . Qiu. , Marlon Pierce, and Geoffrey Fox. School of Informatics and Computing. Pervasive Technology Institute. Indiana University. S. A. L. S. A. project. . http://. salsahpc.indiana.edu. August Shi. , Alex Gyori, Owolabi Legunsen, Darko Marinov. 4/12/2016. ICST 2016. Chicago, Illinois. CCF-1012759. , CCF-1409423, . CCF-1421503, CCF-1439957. Example Code and Test. 2. public. . class. 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 . and . Robust . Scalable Data mining . for . the Data Deluge . Petascale Data Analytics: Challenges, and Opportunities (PDAC-11. ). Workshop at SC11 Seattle. November 14 2011. Geoffrey Fox. gcf@indiana.edu. Matthew Kelly. April 12, 2011. What is Annealing?. Slow cooling of a heated substance allows atoms to line themselves up, creating a stronger structure with minimum energy. Accelerating the cooling process produces a structure with more energy and fewer atoms optimally aligned.. Rohit. Ray. ESE 251 . The Problem. Most . minimization (maximization) strategies . work to find the nearest local minimum. Trapped at local . minimums (maxima). Standard strategy. Generate trial point based on current estimates. 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. 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.