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» Parallel k h-Means Clustering for Large Data Sets
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HIS
2004
15 years 7 months ago
Adaptive Boosting with Leader based Learners for Classification of Large Handwritten Data
Boosting is a general method for improving the accuracy of a learning algorithm. AdaBoost, short form for Adaptive Boosting method, consists of repeated use of a weak or a base le...
T. Ravindra Babu, M. Narasimha Murty, Vijay K. Agr...
APPT
2005
Springer
15 years 11 months ago
Principal Component Analysis for Distributed Data Sets with Updating
Identifying the patterns of large data sets is a key requirement in data mining. A powerful technique for this purpose is the principal component analysis (PCA). PCA-based clusteri...
Zheng-Jian Bai, Raymond H. Chan, Franklin T. Luk
IASSE
2004
15 years 7 months ago
A Model for Multi-relational Data Mining on Demand Forecasting
Accurate demand forecasting remains difficult and challenging in today's competitive and dynamic business environment, but even a little improvement in demand prediction may ...
Qin Ding, Bhavin Parikh
GRID
2004
Springer
15 years 11 months ago
High Performance Threaded Data Streaming for Large Scale Simulations
We have developed a threaded parallel data streaming approach using Logistical Networking (LN) to transfer multi-terabyte simulation data from computers at NERSC to our local anal...
Viraj Bhat, Scott Klasky, Scott Atchley, Micah Bec...
EUROPAR
2003
Springer
15 years 11 months ago
A Parallel Algorithm for Incremental Compact Clustering
In this paper we propose a new parallel clustering algorithm based on the incremental construction of the compact sets of a collection of objects. This parallel algorithm is portab...
Reynaldo Gil-García, José Manuel Bad...