Introduction - Data Mining: Concepts and Techniques - page 37

There are also many topics on data warehouse technology, systems, and applica-
tions, such as The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
by Kimball and Ross [KR02]; The Data Warehouse Lifecycle Toolkit by Kimball, Ross,
Thornthwaite, and Mundy [KRTM08]; Mastering Data Warehouse Design: Relational
and Dimensional Techniques by Imhoff, Galemmo, and Geiger [IGG03]; and Building
the Data Warehouse by Inmon [Inm96]. A set of research papers on materialized views
and data warehouse implementations were collected in Materialized Views: Techniques,
Implementations, and Applications by Gupta and Mumick [GM99]. Chaudhuri and
Dayal [CD97] present an early comprehensive overview of data warehouse technology.
Research results relating to data mining and data warehousing have been pub-
lished in the proceedings of many international database conferences, including the
ACM-SIGMOD International Conference on Management of Data (SIGMOD), the
International Conference on Very Large Data Bases (VLDB), the ACM SIGACT-
SIGMOD-SIGART Symposium on Principles of Database Systems (PODS), the Inter-
national Conference on Data Engineering (ICDE), the International Conference on
Extending Database Technology (EDBT), the International Conference on Database
Theory (ICDT), the International Conference on Information and Knowledge Man-
agement (CIKM), the International Conference on Database and Expert Systems Appli-
cations (DEXA), and the International Symposium on Database Systems for Advanced
Applications (DASFAA). Research in data mining is also published in major database
journals, such as IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM
Transactions on Database Systems (TODS), Information Systems, The VLDB Journal,
Data and Knowledge Engineering , International Journal of Intelligent Information Systems
(JIIS) , and Knowledge and Information Systems (KAIS) .
Many effective data mining methods have been developed by statisticians and intro-
duced in a rich set of textbooks. An overview of classification from a statistical pattern
recognition perspective can be found in Pattern Classification by Duda, Hart, and Stork
[DHS01]. There are also many textbooks covering regression and other topics in statis-
tical analysis, such as Mathematical Statistics: Basic Ideas and Selected Topics by Bickel
and Doksum [BD01]; The Statistical Sleuth: A Course in Methods of Data Analysis by
Ramsey and Schafer [RS01]; Applied Linear Statistical Models by Neter, Kutner, Nacht-
sheim, and Wasserman [NKNW96]; An Introduction to Generalized Linear Models by
Dobson [Dob90]; Applied Statistical Time Series Analysis by Shumway [Shu88]; and
Applied Multivariate Statistical Analysis by Johnson and Wichern [JW92].
Research in statistics is published in the proceedings of several major statistical con-
ferences, including Joint Statistical Meetings, International Conference of the Royal
Statistical Society and Symposium on the Interface: Computing Science and Statistics.
Other sources of publication include the Journal of the Royal Statistical Society , The
Annals of Statistics , the Journal of American Statistical Association , Technometrics , and
Biometrika .
Textbooks and reference topics on machine learning and pattern recognition include
Machine Learning by Mitchell [Mit97]; Pattern Recognition and Machine Learning by
Bishop [Bis06]; Pattern Recognition by Theodoridis and Koutroumbas [TK08]; Introduc-
tion to Machine Learning by Alpaydin [Alp11]; Probabilistic Graphical Models: Principles