Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Research problems in data warehousing
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Hypergraph based reorderings of outer join queries with complex predicates
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Outerjoin simplification and reordering for query optimization
ACM Transactions on Database Systems (TODS)
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A unifying review of linear Gaussian models
Neural Computation
The PanQ tool and EMF SQL for complex data management
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
NonStop SQL/MX primitives for knowledge discovery
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Extending the database relational model to capture more meaning
ACM Transactions on Database Systems (TODS)
SQLEM: fast clustering in SQL using the EM algorithm
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A relational model of data for large shared data banks
Communications of the ACM
SQL database primitives for decision tree classifiers
Proceedings of the tenth international conference on Information and knowledge management
FREM: fast and robust EM clustering for large data sets
Proceedings of the eleventh international conference on Information and knowledge management
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Spreadsheets in RDBMS for OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Clustering binary data streams with K-means
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Processing frequent itemset discovery queries by division and set containment join operators
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Clustering gene expression data in SQL using locally adaptive metrics
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Vertical and horizontal percentage aggregations
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Efficient Disk-Based K-Means Clustering for Relational Databases
IEEE Transactions on Knowledge and Data Engineering
COMBI-operator - database support for data mining applications
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
ATLAS: a small but complete SQL extension for data mining and data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Vector and matrix operations programmed with UDFs in a relational DBMS
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Efficient computation of PCA with SVD in SQL
Proceedings of the 2nd Workshop on Data Mining using Matrices and Tensors
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In a data mining project, a significant portion of time is devoted to building a data set suitable for analysis. In a relational database environment, building such data set usually requires joining tables and aggregating columns with SQL queries. Existing SQL aggregations are limited since they return a single number per aggregated group, producing one row for each computed number. These aggregations help, but a significant effort is still required to build data sets suitable for data mining purposes, where a tabular format is generally required. This work proposes very simple, yet powerful, extensions to SQL aggregate functions to produce aggregations in tabular form, returning a set of numbers instead of one number per row. We call this new class of functions horizontal aggregations. Horizontal aggregations help building answer sets in tabular form (e.g. point-dimension, observation-variable, instance-feature), which is the standard form needed by most data mining algorithms. Two common data preparation tasks are explained, including transposition/aggregation and transforming categorical attributes into binary dimensions. We propose two strategies to evaluate horizontal aggregations using standard SQL. The first strategy is based only on relational operators and the second one uses the "case" construct. Experiments with large data sets study the proposed query optimization strategies.