Fundamental clustering algorithms suite

GeoSOM Suite: A Tool for Spatial Clustering

The large amount of spatial data available today demands the use of data mining tools for its analysis. One of the most used data mining techniques is clustering. Several methods for spatial clustering exist, but many consider space as just another variable. We present in this paper a tool particularly suited for spatial clustering: the GeoSOM suite. This tool implements the GeoSOM algorithm, w...

Fundamental CAD algorithms

Computer-aided design (CAD) tools are now making it possible to automate many aspects of the design process. This has mainly been made possible by the use of effective and efficient algorithms and corresponding software structures. The very large scale integration (VLSI) design process is extremely complex, and even after breaking the entire process into several conceptually easier steps, it ha...

Fundamental Parallel Algebraic Algorithms

Online Learning: Fundamental Algorithms

The Learning Theory 1, is somewhat least practical part of Machine Learning, which is most about the theoretical guarantees in learning concepts, in different conditions and scenarios. These guarantees are usually expressed in the form of probabilistic concentration of some measure, around some optimal value which is unknown and need to discovered. These bounds are functions of problem specific...

Fundamental Limitations of Spectral Clustering

Spectral clustering methods are common graph-based approaches to clustering of data. Spectral clustering algorithms typically start from local information encoded in a weighted graph on the data and cluster according to the global eigenvectors of the corresponding (normalized) similarity matrix. One contribution of this paper is to present fundamental limitations of this general local to global...