PPT-Large-scale Single-pass k-Means Clustering at Scale
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Largescale Singlepass kMeans Clustering Largescale k Means Clustering Goals Cluster very large data sets Facilitate large nearest neighbor search Allow very large
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Large-scale Single-pass k-Means Clustering at Scale: Transcript
Largescale Singlepass kMeans Clustering Largescale k Means Clustering Goals Cluster very large data sets Facilitate large nearest neighbor search Allow very large number of clusters Achieve good quality. Margareta Ackerman. Work with . Shai. Ben-David, . Simina. . Branzei. , and David . Loker. . Clustering is one of the most widely used tools for exploratory data analysis.. . Social Sciences. Biology. 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. Machine . Learning . 10-601. , Fall . 2014. Bhavana. . Dalvi. Mishra. PhD student LTI, CMU. Slides are based . on materials . from . Prof. . Eric Xing, Prof. . . William Cohen and Prof. Andrew Ng. CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Clustering Techniques and IR. Today. Clustering Problem and Applications. Clustering Methodologies and Techniques. Applications of Clustering in IR. Margareta Ackerman. Work with . Shai. Ben-David, . Simina. . Branzei. , and David . Loker. . Clustering is one of the most widely used tools for exploratory data analysis.. . Social Sciences. Biology. 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 . David Kauchak. CS . 158. . – Fall . 2016. Administrative. Final project. Presentations on . Tuesday. 4. . minute max. 2. -. 3. slides. . . E-mail me by . 9am . on . Tuesday. What problem you tackled and results. Clustering. . Unsupervised Learning. Clustering, Informal Goals. Goal. : . Automatically . partition . unlabeled. . data into groups of similar . datapoints. .. . Question. : When and why would we want to do this?. 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 . Gettysburg College. Laura E. Brown. Michigan . Technological University. Outline. Unsupervised versus Supervised Learning. Clustering Problem. k. -Means Clustering Algorithm. Visual. Example. Worked Example. 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.
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