CE: the Classifier-Estimator Framework for Data Mining

The aim of this research is to establish a coherent framework for data mining Observing that data mining depends on two partitions, the classifier and the estimator, this paper defines the classifier/estimator (CE) framework. The classifier indicates the target of the data mining investigation. The classifier may be difficult to express from the data instance or may involve an “oracle” beyond the extant data. The estimator is typically simply expressible using the data instance. The degree to which the estimator refines the classifier partition can be used to measure how well the data instance matches the concept being investigated.

[1]  Claude Delobel,et al.  Normalization and hierarchical dependencies in the relational data model , 1978, TODS.

[2]  Heikki Mannila,et al.  A database perspective on knowledge discovery , 1996, CACM.

[3]  T. Imielinski,et al.  A database perspective on knowledge discovery : A database perspective on knowledge discovery , 1996 .

[4]  Ronald Fagin,et al.  Multivalued dependencies and a new normal form for relational databases , 1977, TODS.

[5]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[6]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[7]  Evangelos Simoudis,et al.  Reality Check for Data Mining , 1996, IEEE Expert.

[8]  Egon Börger,et al.  Trends in theoretical computer science , 1988 .

[9]  H. Mannila,et al.  Data mining: machine learning, statistics, and databases , 1996, Proceedings of 8th International Conference on Scientific and Statistical Data Base Management.

[10]  Heikki Mannila,et al.  Efficient Algorithms for Discovering Association Rules , 1994, KDD Workshop.

[11]  Serge Abiteboul,et al.  Foundations of Databases , 1994 .

[12]  Catriel Beeri,et al.  A complete axiomatization for functional and multivalued dependencies in database relations , 1977, SIGMOD '77.

[13]  Roman Slowinski,et al.  Rough-Set Reasoning about Uncertain Data , 1996, Fundam. Informaticae.

[14]  E. F. CODD,et al.  A relational model of data for large shared data banks , 1970, CACM.

[15]  Heikki Mannila,et al.  Methods and Problems in Data Mining , 1997, ICDT.

[16]  Jeffrey D. Uuman.  Principles of database and knowledge- base systems , 1989 .

[17]  Heikki Mannila,et al.  Approximate Dependency Inference from Relations , 1992, ICDT.

[18]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[19]  E. F. Codd,et al.  Relational Completeness of Data Base Sublanguages , 1972, Research Report / RJ / IBM / San Jose, California.

[20]  W. W. Armstrong,et al.  Dependency Structures of Data Base Relationships , 1974, IFIP Congress.

[21]  Abraham Silberschatz,et al.  On Subjective Measures of Interestingness in Knowledge Discovery , 1995, KDD.