About the Book
Data mining (the analysis step of the Knowledge Discovery in Databases process, or KDD), a relatively young and interdisciplinary field of computer science, is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management.With recent technical advances in processing power, storage capacity, and inter-connectivity of computer technology, data mining is seen as an increasingly important tool by modern business to transform unprecedented quantities of digital data into business intelligence giving an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery. The growing consensus that data mining can bring real value has led to an explosion in demand for novel data mining technologies.
This book is your ultimate resource for Data Mining. Here you will find the most up-to-date information, analysis, background and everything you need to know.
In easy to read chapters, with extensive references and links to get you to know all there is to know about Data Mining right away, covering: Data mining, Able Danger, Accuracy paradox, Affinity analysis, Alpha algorithm, Anomaly detection, Apatar, Apriori algorithm, Association rule learning, Automatic distillation of structure, Ball tree, Biclustering, Big data, Biomedical text mining, Business analytics, CANape, Cluster analysis, Clustering high-dimensional data, Co-occurrence networks, Concept drift, Concept mining, Consensus clustering, Correlation clustering, Cross Industry Standard Process for Data Mining, Cyber spying, Data Applied, Data classification (business intelligence), Data dredging, Data fusion, Data mining agent, Data Mining and Knowledge Discovery, Data mining in agriculture, Data mining in meteorology, Data stream mining, Data visualization, DataRush Technology, Decision tree learning, Deep Web Technologies, Document classification, Dynamic itemset counting, Early stopping, Educational data mining, Elastic map, Environment for DeveLoping KDD-Applications Supported by Index-Structures, Evolutionary data mining, Extension neural network, Feature Selection Toolbox, FLAME clustering, Formal concept analysis, General Architecture for Text Engineering, Group method of data handling, GSP Algorithm, In-database processing, Inference attack, Information Harvesting, Institute of Analytics Professionals of Australia, K-optimal pattern discovery, Keel (software), KXEN Inc., Languageware, Lattice Miner, Lift (data mining), List of machine learning algorithms, Local outlier factor, Molecule mining, Nearest neighbor search, Neural network, Non-linear iterative partial least squares, Open source intelligence, Optimal matching, Overfitting, Principal component analysis, Profiling practices, RapidMiner, Reactive Business Intelligence, Receiver operating characteristic, Ren-rou, Sequence mining, Silhouette (clustering), Software mining, Structure mining, Talx, Text corpus, Text mining, Transaction (data mining), Weather data mining, Web mining, Weka (machine learning), Zementis Inc.
This book explains in-depth the real drivers and workings of Data Mining. It reduces the risk of your technology, time and resources investment decisions by enabling you to compare your understanding of Data Mining with the objectivity of experienced professionals.
