Wiley India Pvt Ltd
|Number of Pages
Data is bits of facts. Facts can be useful, but they must be organized and collated together in a cohesive form to make them meaningful, and then analyzed to extract further useful information out of them. There are a number of techniques used in the management and analysis of data to extract information. Data mining is one of the techniques used.
Just as conventional mining helps humans extract rare minerals from deep within the earth, data mining helps an organization extract useful information from deep within the data stores that it has built up over the years.
Data Mining Methods And Models explains the processes and applications of data mining to its readers, with the use of examples wherever relevant.
The book is divided into seven parts. The first part introduces Dimension Reduction Methods. The second part goes into Regression Modeling, while the third part covers Multiple Regression and Model Building.
The fourth part explains Logistic Regression and the next one goes into naive Bayes and Bayesian Networks. The sixth part covers Genetic Algorithms, while the final part then provides a case study titled Modeling Response to Direct-Mail Marketing.
Data Mining Methods And Models explains the different methods and models of data mining, such as Statistical Inference, Clustering, K-nearest neighbor, Association Rules, Neural Networks, Multivariate Analysis, and Logistic Regression. It uses the Cross Industry Standard Process (CRISP) approach.
Open source WEKA software as well as the SPSS and MiniTab statistical applications are also discussed. Concepts like the working of different data mining algorithms, the newest methods used to mine deeper and extract rich sets of information, and practical experience in using data mining techniques are also explored.
Data Mining Methods And Models lays stress on a deep understanding of the inner workings of data mining techniques, to ensure that students clearly understand the technology involved and gain confidence in using it in real life situations.
The author clearly explains various algorithms related to the subject and demonstrates how to use them on large data sets. Several challenging exercises are provided throughout the text to deepen the students' comprehension of the concepts. The book also has a companion website with additional resources for students and instructors.
About Daniel T. Larose
Daniel T. Larose is a Professor of Statistics.
Other books by this writer include Discovering Statistics, Data Mining The Web: Uncovering Patterns In Web Content, Structure, And Usage, and Discovering Knowledge In Data: An Introduction To Data Mining.
Larose writes textbooks on statistics and data mining.
He is on the faculty of the Central Connecticut State University. Larose is also the Director of the first online master's degree course in data mining, Data Mining@CCSU. He has a doctorate from the University of Connecticut and is a consultant to many companies in the Connecticut region.