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Coles DATA MINING and MACHINE LEARNING: CLUSTER ANALYSIS and kNN CLASSIFIERS. Examples with MATLAB at Bayshore Shopping Centre in Ottawa, ON
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Data Mining an Machine Learning uses two types of techniques: predictive techniques (supervised learnig techniques) , which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques (unsupervised learning techniques), which finds hidden patterns or intrinsic structures in input data. Descriptive techniques finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common descriptive technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition. This book develops classification descriptive techniques (unsupervised learning techniques) related to cluster analysis and kNN classifiers.
Data Mining an Machine Learning uses two types of techniques: predictive techniques (supervised learnig techniques) , which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques (unsupervised learning techniques), which finds hidden patterns or intrinsic structures in input data. Descriptive techniques finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common descriptive technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition. This book develops classification descriptive techniques (unsupervised learning techniques) related to cluster analysis and kNN classifiers.
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