PDF-Constrained KMeans Clustering

S Bradley KP Bennett ADemiriz Microsoft Researc Dept of Mathematical Sciences One Microsoft W Dept of Decision Sciences and Eng Sys Redmond W A 98052 Renselaer P olytec hnic Institute ro NY 12180 br ad ley micr osoftc om ennekdemir r. cornelledu Thorsten Joachims Department of Computer Science Cornell University Ithaca NY USA tjcscornelledu ABSTRACT The means clustering algorithm is one of the most widely used e64256ective and best understood clustering methods How ever successful Sculley Google Inc Pittsburgh PA USA dsculleygooglecom ABSTRACT We present two modi57356cations to the popular means clus tering algorithm to address the extreme requirements for latency scalability and sparsity enco Although it o64256ers no accuracy guarantees its simplicity and speed are very appealing in practice By augmenting kmeans with a very simple ran domized seeding technique we obtain an algorithm that is 920log competitive with the optimal clustering 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. Zhiyao. . Duan. , . Jinyu. Han and Bryan . Pardo. EECS Dept., Northwestern Univ.. Interactive Audio Lab, . http://music.cs.northwestern.edu. For presentation in ICASSP 2010, Dallas, Texas, USA.. Multi-pitch Estimation & Tracking Task. 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. 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 . 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”. 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 . Zhiyao. . Duan. , . Jinyu. Han and Bryan . Pardo. EECS Dept., Northwestern Univ.. Interactive Audio Lab, . http://music.cs.northwestern.edu. For presentation in ICASSP 2010, Dallas, Texas, USA.. Multi-pitch Estimation & Tracking Task. 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.. 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.