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There are several techniques that can be used for mining patterns, trends, and anomalies in data. These include data visualization, machine learning algorithms, clustering, classification, regression, association rules, and sequential patterns. These techniques can help to identify patterns and trends in the data, and can also be used to detect anomalies or outliers.
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Visual discovery is the most complicated quadrant because it consists of two categories: testing a hypothesis and mining for patterns, trends and anomalies. Berinato says: "The former is focused, whereas the latter is more flexible. The bigger and more complex the data, and the less you know going in, the more open-ended the work."
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