Clustering is one of most important building fields in data mining and in machine learning in general. Most clustering algorithms is designed for off-line (or batch) processing, in which the clustering process repeatedly sweeps through the set of data samples in order to capture its underlying structure in a compact and efficient way. However, with the continuous increment of set of data sample...

We present a fast online clustering algorithm which has linear worst-case time complexity and constant running time average for the well-known online visually oriented browsing modeling called Scatter/Gather browsing (Cutting, Karger, Pedersen, and Tukey 1992). Our experiment shows when running on a single processor, this fast online clustering algorithm is few hundred times faster than the par...

Introduction. Continuous learning and online decisionmaking in complex dynamic environments under conditions of uncertainty and limited computational recourses represent one of the most challenging problems for developing robust intelligent systems. The existing task of unsupervised clustering in statistical learning requires the maximizing (or minimizing) of a certain similarity-based objectiv...

Many modern clustering methods scale well to a large number of data items, N , but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy algorithm for online hierarchical clustering that scales to both massive N and K—a problem setting we term extreme clustering. Our algorithm efficiently routes new data points to the leaves of an incrementally-built tree. Motivate...

In this paper is presented a new model for data clustering, which is inspired from the selfassembly behavior of real ants. Real ants can build complex structures by connecting themselves to each others. It is shown is this paper that this behavior can be used to build a hierarchical tree-structured partitioning of the data according to the similarities between those data. Several algorithms hav...