High performance data mining (tutorial PM-3)
Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Object and query transformation: supporting multi-dimensional queries through code reuse
Proceedings of the ninth international conference on Information and knowledge management
Systems support for scalable data mining
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
Introduction: Recent Developments in Parallel and Distributed Data Mining
Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Parallel data intensive computing in scientific and commercial applications
Parallel Computing - Parallel data-intensive algorithms and applications
Parallel Implementation of Decision Tree Learning Algorithms
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
KNOWLEDGE GRID: High Performance Knowledge Discovery on the Grid
GRID '01 Proceedings of the Second International Workshop on Grid Computing
Clusters and Grids for Distributed and Parallel Knowledge Discovery
HPCN Europe 2000 Proceedings of the 8th International Conference on High-Performance Computing and Networking
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
A Requirements Analysis for Parallel KDD Systems
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
Scalable Parallel Clustering for Data Mining on Multicomputers
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
Parallelism in Knowledge Discovery Techniques
PARA '02 Proceedings of the 6th International Conference on Applied Parallel Computing Advanced Scientific Computing
Mining Comprehensible Rules from Data with an Ant Colony Algorithm
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
A Genetic Algorithm-Based Solution for the Problem of Small Disjuncts
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
A Concurrent Approach to the Key-Preserving Attribute-Oriented Induction Method
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Parallel and Distributed Data Mining: An Introduction
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
A Data-Clustering Algorithm on Distributed Memory Multiprocessors
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
P-AutoClass: Scalable Parallel Clustering for Mining Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
Data mining tasks and methods: Classification: decision-tree discovery
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
Lessons and Challenges from Mining Retail E-Commerce Data
Machine Learning
Multiagent-Based Model Integration
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Exploiting idle cycles to execute data mining applications on clusters of PCs
Journal of Systems and Software
Distributed Nearest Neighbor-Based Condensation of Very Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
Generating classification association rules with modified Apriori algorithm
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Distributed and Shared Memory Algorithm for Parallel Mining of Association Rules
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Using Distributed Data Mining and Distributed Artificial Intelligence for Knowledge Integration
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
Wise mining method through ant colony optimization
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Efficient Distributed Genetic Algorithm for Rule extraction
Applied Soft Computing
Parallel boosted regression trees for web search ranking
Proceedings of the 20th international conference on World wide web
Proceedings of the Third Workshop on Large Scale Data Mining: Theory and Applications
Comparing meta-learning algorithms
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Generic pattern mining via data mining template library
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Evolutionary k-means for distributed data sets
Neurocomputing
| Hi-index | 0.00 |
From the Publisher:Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely: "intelligent" (machine learning-based) data mining techniques; relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. It is assumed that the reader has a knowledge roughly equivalent to a first degree (B.Sc.) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience of Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and post-graduate students, particularly database researchers interested in advanced, intelligent database applications and artificial intelligence researchers interested in industrial, real-world applications of machine learning.