Data Mining Principles, Process Model and Applications
Book provides sound knowledge of data mining principles, algorithms, machine learning, data mining process models, applications, and experiments done on open source tool WEKA.
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Data Mining Algorithms
Extension for Medical Domain
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analysis application attributes based algorithms Boolean build model categorical Clustered instances confusion matrix CRISP-DM cross-validation data mining data mining algorithms Data preparation Data understanding DBscan decision tree Evaluation of classifiers Evaluation of clusterer extract Full Cross Full Percentage Full training data Full training set healthcare hierarchical image recognition data IMDB input integer iteration k-means k-means algorithm knowledge discovery Labor data set learning Letter image recognition Linear Regression Mean absolute error mean squared error methodology missing data Movie rating neural network Number of Instances outliers output partitioning patient patterns Percenatge performed phase prediction problem process mining process model recognition data set Relative absolute error relative squared error Root mean squared Root relative squared Rotten Tomatoes selection SEMMA set validation set with percentage step Supermarket data set supervised learning taken to build techniques test set total number training tuples tweets twitter variable visualization WEKA Zoo data set