Databases Reference
In-Depth Information
framework for the development, evaluation, and practice of data mining technology.
Several theories for the basis of data mining include the following:
Data reduction
: In this theory, the basis of data mining is to reduce the data rep-
resentation. Data reduction trades accuracy for speed in response to the need to
obtain quick approximate answers to queries on very large databases. Data reduc-
tion techniques include singular value decomposition (the driving element behind
principal components analysis), wavelets, regression, log-linear models, histograms,
clustering, sampling, and the construction of index trees.
Data compression
: According to this theory, the basis of data mining is to compress
the given data by encoding in terms of bits, association rules, decision trees, clusters,
and so on. Encoding based on the
minimum description length principle
states that
the “best” theory to infer from a data set is the one that minimizes the length of the
theory and of the data when encoded, using the theory as a predictor for the data.
This encoding is typically in bits.
Probability and statistical theory
: According to this theory, the basis of data min-
ing is to discover joint probability distributions of random variables, for example,
Bayesian belief networks or hierarchical Bayesian models.
Microeconomic view
: The microeconomic view considers data mining as the task
of finding patterns that are interesting only to the extent that they can be used in
the decision-making process of some enterprise (e.g., regarding marketing strategies
and production plans). This view is one of utility, in which patterns are considered
interesting if they can be acted on. Enterprises are regarded as facing optimization
problems, where the object is to maximize the utility or value of a decision. In this
theory, data mining becomes a nonlinear optimization problem.
Pattern discovery and inductive databases
: In this theory, the basis of data mining
is to discover patterns occurring in the data such as associations, classification mod-
els, sequential patterns, and so on. Areas such as machine learning, neural network,
association mining, sequential pattern mining, clustering, and several other subfields
contribute to this theory. A knowledge base can be viewed as a database consisting
of data and patterns. A user interacts with the system by querying the data and the
theory (i.e., patterns) in the knowledge base. Here, the knowledge base is actually an
inductive database.
These theories are not mutually exclusive. For example, pattern discovery can also
be seen as a form of data reduction or data compression. Ideally, a theoretical frame-
work should be able to model typical data mining tasks (e.g., association, classification,
and clustering), have a probabilistic nature, be able to handle different forms of data,
and consider the iterative and interactive essence of data mining. Further efforts are
required to establish a well-defined framework for data mining that satisfies these
requirements.
