About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 103. Chapters: Data mining, Text corpus, Principal component analysis, Overfitting, Able Danger, Cluster analysis, Neural network, Receiver operating characteristic, Association rule learning, Open source intelligence, Profiling practices, Text mining, Formal concept analysis, Nearest neighbor search, Data visualization, Multifactor dimensionality reduction, Decision tree learning, Biclustering, General Architecture for Text Engineering, Molecule mining, Concept drift, Biomedical text mining, Data dredging, Web mining, Consensus clustering, Weka, Clustering high-dimensional data, Group method of data handling, Jumper 2.0, Data stream mining, Data fusion, Lattice Miner, Knowledge tags, Data mining in agriculture, Environment for DeveLoping KDD-Applications Supported by Index-Structures, CANape, Elastic map, Business analytics, Cyber spying, RapidMiner, Pervasive DataRush Technology, Big data, Feature Selection Toolbox, Local Outlier Factor, Co-occurrence networks, Cluster-weighted modeling, Correlation clustering, Unstructured data, Optimal matching, Alpha algorithm, Educational data mining, Structure mining, Languageware, Apriori algorithm, Accuracy paradox, FLAME clustering, Evolutionary data mining, Document classification, Knowledge discovery, Apatar, Cross Industry Standard Process for Data Mining, Affinity analysis, Zementis Inc, Anomaly detection, Software mining, Health Research Development Information Network, Early stopping, Silhouette, Lift, GSP Algorithm, Talx, Reactive Business Intelligence, Concept mining, KXEN Inc., Data classification, Institute of Analytics Professionals of Australia, In-database processing, List of machine learning algorithms, Keel, Inference attack, Non-linear iterative partial least squares, Deep Web Technologies, Sequence mining, K-optimal pattern discovery, Information Harvesting, Automatic d...
