Algorithms for clustering data
Algorithms for clustering data
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Parallel algorithms for hierarchical clustering
Parallel Computing
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data preparation for data mining
Data preparation for data mining
Automating exploratory data analysis for efficient data mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling mining algorithms to large databases
Communications of the ACM - Evolving data mining into solutions for insights
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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An automated data mining service offers an out-sourced, cost-effective analysis option for clients desiring to leverage their data resources for decision support and operational improvement. In the context of the service model, typically the client provides the service with data and other information likely to aid in the analysis process (e.g. domain knowledge, etc.). In return, the service provides analysis results to the client. We describe the required processes, issues, and challenges in automating the data mining and analysis process when the high-level goals are: (1) to provide the client with a high quality, pertinent analysis result; and (2) to automate the data mining service, minimizing the amount of human analyst effort required and the cost of delivering the service. We argue that by focusing on client problems within market sectors, both of these goals may be realized.