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34 Chapter 1 Introduction 2011/6/1 3:12 Page 34 #34

Multidimensional data mining (also called exploratory multidimensional data
mining) integrates core data mining techniques with OLAP-based multidimen-
sional analysis. It searches for interesting patterns among multiple combinations
of dimensions (attributes) at varying levels of abstraction, thereby exploring multi-
dimensional data space.
Data mining functionalities are used to specify the kinds of patterns or knowledge
to be found in data mining tasks. The functionalities include characterization and
discrimination; the mining of frequent patterns, associations, and correlations; clas-
sification and regression; cluster analysis; and outlier detection. As new types of data,
new applications, and new analysis demands continue to emerge, there is no doubt
we will see more and more novel data mining tasks in the future.
Data mining, as a highly application-driven domain, has incorporated technologies
from many other domains. These include statistics, machine learning, database and
data warehouse systems, and information retrieval. The interdisciplinary nature of
data mining research and development contributes significantly to the success of
data mining and its extensive applications.
Data mining has many successful applications, such as business intelligence, Web
search, bioinformatics, health informatics, finance, digital libraries, and digital
governments.
There are many challenging issues in data mining research. Areas include mining
methodology, user interaction, efficiency and scalability, and dealing with diverse
data types. Data mining research has strongly impacted society and will continue to
do so in the future.
1.9 Exercises

1.1 What is data mining? In your answer, address the following:
(a) Is it another hype?
(b) Is it a simple transformation or application of technology developed from databases,
statistics, machine learning, and pattern recognition?
(c) We have presented a view that data mining is the result of the evolution of database
technology. Do you think that data mining is also the result of the evolution of
machine learning research? Can you present such views based on the historical
progress of this discipline? Address the same for the fields of statistics and pattern
recognition.
(d) Describe the steps involved in data mining when viewed as a process of knowledge
discovery.
1.2 How is a data warehouse different from a database? How are they similar?
1.3 Define each of the following data mining functionalities: characterization, discrimi-
nation, association and correlation analysis, classification, regression, clustering, and