PPT-Clustering

Basic Concepts and Algorithms Bamshad Mobasher DePaul University 2 What is Clustering in Data Mining Cluster a collection of data objects that are similar to one another and thus can be treated collectively as one group. 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. . Ensemble Clustering. unlabeled . data. ……. F. inal . partition. clustering algorithm 1. combine. clustering algorithm . N. ……. clustering algorithm 2. Combine multiple partitions of . given. data . 1. Unsupervised Learning and Clustering. In unsupervised learning you are given a data set with no output classifications. Clustering is an important type of unsupervised learning. PCA was another type of unsupervised learning. Writing To Learn In All Content Areas. What is Clustering?. Clustering is a way to organize information and make associations or connections between those ideas.. The Chicken Convention. Clustering. Not a new technique. and Physical Interaction . Datasets. Manikandan Narayanan, Adrian Vetta, Eric E. Schadt, Jun Zhu. PLoS Computational Biology 2010. Presented by: Tal Saiag. Seminar in Algorithmic Challenges in Analyzing Big Data* in Biology and . M. Soltanolkotabi E.Elhamifar E.J. Candes. 报告. 人：万晟、元玉慧. 、. 张. 驰. 昱. 信息科学与技术学院. 智. 能科学系. 1. Main Contribution. Existing work. Subspace Clustering. extratropical. cyclones: their influence on extreme precipitation events in the . UK. Suzanne Gray. Ruari. Rhodes. , Len . Shaffrey. Jointly sponsored . by . University of Reading and Lloyds Banking Group. 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. 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. 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. 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.