BorderFlow implements a general-purpose graph clustering algorithm. It maximizes the inner to outer flow ratio from the border of each cluster to the rest of the graph. The main advantage of the algorithm is that it does not need parametrization to compute results of high accuracy.
ClodHopper is a Java library for high-performance clustering of numerical data. It contains clustering implementations such as K-Means, K-Means++, X-Means, G-Means, Fuzzy C-Means, Jarvis-Patrick, and various forms of hierarchical clustering. ClodHopper's clustering implementations take advantage of the host system's concurrent processing ability to speed clustering. The data structures are also very lean to conserve memory usage. ClodHopper is very extensible. If you are developing a new clustering algorithm, you may save yourself an enormous amount of work by extending a ClodHopper base class.
CloudVPN is a secure decentralized mesh networking tool. It allows applications to use it as a mesh transport layer for packet routing, easily creating mesh ethernet VPN, secured audio/video broadcasting or communication channels, etc. It can create secured networks with special or weird topologies, so it's very easy to create connection schemes with clustered/decentralized servers, topologies with better throughput, ring-like topologies for failover, long-line for passing through many routes, or tree topology for optimizing inter-server bandwidth needs.
Crash Dummy is a Java Web application to help IT professionals set up Java application server environments. It has several features to help make this easier, including simulating failures and diagnostics. Crash Dummy is particularly helpful for setting up complex clustered environments and monitoring infrastructure.
Hados stores files in a cluster of servers. Its goal is to handle high availability by storing copies of the same file on several nodes. It provides RESTFUL APIs to easily store, check, or retrieve files. Using the cluster APIs, you can retrieve files from whichever node hosts them. To avoid any single point of failure, it is possible to apply a request to any node of the cluster; there is no master node.
K-tree provides a scalable approach to clustering by combining the B+-tree and k-means algorithms. Clustering can be used to solve problems in signal processing, machine learning, and other contexts. It has recently been used to solve document clustering problems on the Wikipedia collection.
Lucie is a cluster installation and configuration tool. It enables parallel network installation of large numbers of nodes from one single administration server. The Lucie installer performs HDD partitioning and installations of the Linux kernel and required software packages. The Lucie configurator then generates system and software configurations. Lucie is designed to be scalable and efficient, so a complete Linux cluster can be built from scratch in a short amount of time. Moreover, the whole installation process is designed to be fully automated.
MLPACK is a C++ machine learning library with an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. It contains algorithms such as k-means, Gaussian mixture models, hidden Markov models, density estimation trees, kernel PCA, locality-sensitive hashing, sparse coding, linear regression and least-angle regression.