This book deals with the concepts of Data ware housing and mining which helps the students to analyze the data, identify the identify the problems, and choose the relevant models and algorithms to apply. It also further be able to assess the strengths and weaknesses of various methods and algorithms and to analyze their behavior. Students will be enabled to understand and implement classical models and algorithms in data warehousing and data mining. This book not only introduces the fundamentals of Data ware housing and mining, it also explores new and emerging tools and techniques. It explains basic data mining concepts like OLAP, concept description, data preprocessing, classification and prediction, association rules and cluster analysis. It then presents advanced data mining techniques like extracting information from varied and complex sources other than just relational databases. This includes multimedia databases, object databases, time-series databases and spatial databases. It also looks at harvesting data from varied sources on the World Wide Web and extracting useful information from it.
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