Data Mining Desktop Survival Guide
by Graham Williams
Publisher: Togaware Pty Ltd 2004
Data mining is about building models from data. We build models to gain insights into the world and how the world works. A data miner, in building models, deploys many different data analysis and model building techniques. Our choices depend on the business problems to be solved. Although data mining is not the only approach it is becoming very widely used because it is well suited to the data environments we find in today's enterprises.
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by Saed Sayad - University of Toronto
Data Mining is about explaining the past and predicting the future by means of data analysis. Data mining is a multidisciplinary field which combines statistics, machine learning, artificial intelligence and database technology.
by Christian S. Jensen - Aalborg University
Topics covered: the semantics of temporal data, the design of data models and languages for temporal data, the design of databases expressed in terms of temporal data models as well as temporally enhanced design of conventional databases.
by Chuck Ballard, et al. - IBM Redbooks
It covers data modeling techniques for data warehousing, within the context of the overall data warehouse development process. The process of data warehouse modeling, including the steps required before and after the actual modeling, is discussed.
Data mining comprises techniques and algorithms, for determining interesting patterns from large datasets. There are currently hundreds algorithms that perform tasks such as frequent pattern mining, clustering, and classification, among others.