PPT-1 Clustering

2   Partitioning methods Hierarchical methods Densitybased Gridbased Modelbased Clustering Techniques 3 Data Matrix x 11 x 1f x 1p x i1 . 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. . Brendan and Yifang . April . 21 . 2015. Pre-knowledge. We define a set A, and we find the element that minimizes the error. We can think of as a sample of . Where is the point in C closest to X. . Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Nov 3. rd. 2016. RECAP. Suresh Merugu, IITR. Overview. Definition of Clustering. Existing Clustering Methods. Clustering Examples. Classification. Classification Examples. Cluster. : A collection of data objects. Similar to one another within the same cluster. 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 . to . LC-MS 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. gcf@indiana.edu. 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. 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.