Scalable parallel algorithms for surface fitting and data mining

Scalable parallel algorithms for surface fitting and data mining

This paper presents scalable parallel algorithms for high dimensional surface fitting and predictive modelling which are used in data mining applications. These algorithms are based on techniques like finite elements, thin plate splines, wavelets and additive models. They all consist of two steps: First, data is read from secondary storage and a linear system is assembled. Secondly, the linear ...

A parallel finite element surface fitting algorithm for data mining

A major task in data mining is to develop automatic techniques to process and to detect patterns in very large data sets. Multivariate regression techniques form the core of many data mining applications. A common assumption is that the multivariate data is well approximated by an additive model involving only first and second order interaction terms. In this case high-dimensional nonparametric...

Efficient Data Mining: Scripting and Scalable Parallel Algorithms

This paper presents our approach to data mining that allows the coupling of parallel applications with a scripting language resulting in an efficient and flexible toolbox. Parallel algorithms which are scalable both in data size and number of processors are a key issue to be able to solve the ever increasing problems in data mining. On the other hand, data mining applications should be flexible...

Efficient Data Mining with Evolutionary Algorithms for Cloud Computing Application

With the rapid development of the internet, the amount of information and data which are produced, are extremely massive. Hence, client will be confused with huge amount of data, and it is difficult to understand which ones are useful. Data mining can overcome this problem. While data mining is using on cloud computing, it is reducing time of processing, energy usage and costs. As the speed of ...

Parallel Algorithms in Data Mining