Data Mining: Concepts, Models, Methods, and Algorithms
This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.
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analysis analyze approach approximating Apriori algorithm artificial neuron attribute basic binary C4.5 algorithm categorical classification clustering algorithm complex computation contingency table corresponding data mining data warehouse data-mining applications data-mining process data-mining techniques database decision tree defined dimensionality dimensions distance distribution documents estimate Euclidean distance evaluation example frequent itemsets function fuzzy set given in Figure graph graphical HITS algorithm hyperplane initial input iteration large data sets large number linear machine learning matrix measure methodology methods missing values n-dimensional neural networks neuron node nonlinear number of samples optimization outliers output PageRank parameters partition patterns performance phase points predictive preprocessing problem real-world reduced regression representation represented risk functional selected sequence similarity solution space spatial statistical structure subset supervised learning task text mining tion training data set training samples transformation users variables vector visualization Web mining weight µ µ