Videos tagged with Clustering

At myYearbook.com we use RabbitMQ as the core messaging platform for remote asynchronous task processing, cross-application communication and general information routing and delivery. Pushing RabbitMQ's limits in scaling has provided good insight in how to scale RabbitMQ clusters while avoiding RabbitMQ's common pitfalls. In this talk we will cover RabbitMQ basics including common message routi...

This talk will begin with an overview of the Computer Systems group within the College of Engineering and IT at The Australian National University. These fall under the Themes of Bio-Engineering, Robotics, Advanced Runtime Systems, Performance Analysis, Parallel Processing, Operating Systems. Depending on audience interest, projects under the latter three themes will be discussed in detail. The...

Clustering is the task of finding groups in data. While traditional clustering algorithms typically measure similarity between objects by considering all attributes/features/dimensions of data objects, projected clustering algorithms attempt to find clusters that may exist only in subspaces, i.e., subsets of attributes. The problem of finding projected clusters is motivated by the fact that in ...

For large-scale classification problems, the training samples can be clustered beforehand as a downsampling pre-process, and then only the obtained clusters are used for training. Motivated by such assumption, we proposed a classification algorithm, Support Cluster Machine (SCM), within the learning framework introduced by Vapnik. For the SCM, a compatible kernel is adopted such that a similari...

Regularized Kernel Discriminant Analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. The performance of RKDA depends on the selection of kernels. In this paper, we consider the problem of learning an optimal kernel over a convex set of kernels. We show that the kernel learning problem can be formulated as a semidefinite program (SDP) in the binary-cla...

Lecture 5: Parallel Graph Algorithms with MapReduce. See Google: Cluster Computing and MapReduce for slides and other resources.

Lecture 4: Clustering Algorithms with MapReduce. See Google: Cluster Computing and MapReduce for slides and other resources.

Lecture 3: The Google File System. See Google: Cluster Computing and MapReduce for slides and other resources.

Lecture 2: The MapReduce programming model. See Google: Cluster Computing and MapReduce for slides and other resources.

Lecture 1 in a five part series introducing mapreduce and cluster computing. See Google: Cluster Computing and MapReduce for slides and other resources.