PDF-kmeans The Advantages of Careful Seeding David Arthur Sergei Vassilvitskii Abstract The

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. 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 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. 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. 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. Stat 600. Nonlinear DA. We discussed LDA where our . discriminant. boundary was linear. Now, lets consider scenarios where it could be non-linear. We will discuss:. QDA. RDA. MDA. As before all these methods aim to MINIMIZE the probability of misclassification.. Supervised & Unsupervised Learning. Supervised learning. Classification. The number of classes and class labels of data elements in training data is known beforehand. Unsupervised learning. Clustering. Ramona Garner. PM Specialist. East National Technology Support Center. Choosing a Seed Mix. Each disturbed site is unique and seed mixes should reflect the sites:. climate. soils. environmental setting. 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 . at the CMS experiment. Felice Pantaleo. CERN. felice@cern.ch. Overview. Motivations. Heterogeneous Computing. Track seeding on GPUs during Run-3. Run-2 Track Seeding. Online. Offline. Conclusion. 2. 3. Archetypes, Historical Context,. And Synopsis. Powerpoint Menu. Archetypes and Connections. Story Synopsis. Themes and Historical Context. What is a Legend?. a traditional __________tale or collection of related tales popularly regarded as true, but usually contain a mixture of fact and fiction. 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 . 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. 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.. Introduction to Data Mining, 2. nd. Edition. by. Tan, Steinbach, Karpatne, Kumar. Two Types of Clustering. Hierarchical. Partitional algorithms:. Construct various partitions and then evaluate them by some criterion.