Data Mining_ Concepts and Techniques - Jiawei Han [362]
■ Data reduction: In this theory, the basis of data mining is to reduce the data representation. Data reduction trades accuracy for speed in response to the need to obtain quick approximate answers to queries on very large databases. Data reduction 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 mining 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 models, 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 framework 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.
13.2.3. Visual and Audio Data Mining
Visual data mining discovers implicit and useful knowledge from large data sets using data and/or knowledge visualization techniques. The human visual system is controlled by the eyes and brain, the latter of which can be thought of as a powerful, highly parallel processing and reasoning engine containing a large knowledge base. Visual data mining essentially combines the power of these components, making it a highly attractive and effective tool for the comprehension of data distributions, patterns, clusters, and outliers in data.
Visual data mining can be viewed as an integration of two disciplines: data visualization and data mining. It is also closely related to computer graphics, multimedia systems, human–computer interaction, pattern recognition, and high-performance computing. In general, data visualization and data mining can be integrated in the following ways:
■ Data visualization: Data in a database or data warehouse can be viewed at different granularity