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【正文】 Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Figure 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Collaborative Filtering ? Goal: predict what movies/books/… a person may be interested in, on the basis of ? Past preferences of the person ? Other people with similar past preferences ? The preferences of such people for a new movie/book/… ? One approach based on repeated clustering ? Cluster people on the basis of preferences for movies ? Then cluster movies on the basis of being liked by the same clusters of people ? Again cluster people based on their preferences for (the newly created clusters of) movies ? Repeat above till equilibrium ? Above problem is an instance of collaborative filtering, where users collaborate in the task of filtering information to find information of interest 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Other Types of Associations ? Basic association rules have several limitations ? Deviations from the expected probability are more interesting ? ., if many people purchase bread, and many people purchase cereal, quite a few would be expected to purchase both ? We are interested in positive as well as negative correlations between sets of items ? Positive correlation: cooccurrence is higher than predicted ? Negative correlation: cooccurrence is lower than predicted ? Sequence associations / correlations ? ., whenever bonds go up, stock prices go down in 2 days ? Deviations from temporal patterns ? ., deviation from a steady growth ? ., sales of winter wear go down in summer ? Not surprising, part of a known pattern. ? Look for deviation from value predicted using past patterns 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Association Rules (Cont.) ? Rules have an associated support, as well as an associated confidence. ? Support is a measure of what fraction of the population satisfies both the antecedent and the consequent of the rule. ? ., suppose only percent of all purchases include milk and screwdrivers. The support for the rule is milk ? screwdrivers is low. ? Confidence is a measure of how often the consequent is true when the antecedent is true. ? ., the rule bread ? milk has a confidence of 80 percent if 80 percent of the purchases that include bread also include milk. 169。ve Bayesian classifiers assume attributes have independent distributions, and thereby estimate p (d | cj) = p (d1 | cj ) * p (d2 | cj ) * ….* ( p (dn | cj ) ? Each of the p (di | cj ) can be estimated from a histogram on di values for each class cj ? the histogram is puted from the training instances ? Histograms on multiple attributes are more expensive to pute and store 169。 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition DecisionTree Construction Algorithm Procedure GrowTree (S ) Partition (S )。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Best Splits ? Pick best attributes and conditions on which to partition ? The purity of a set S of training instances can be measured quantitatively in several ways. ? Notation: number of classes = k, number of instances = |S|, fraction of instances in class i = pi. ? The Gini measure of purity is defined as [ Gini (S) = 1 ? ? When all instances are in a single class, the Gini value is 0 ? It reaches its maximum (of 1 –1 /k) if each class the same number of instances. k i 1 p2i 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Data Mining (Cont.) ? Descriptive Patterns ? Associations ? Find books that are often bought by “similar” customers. If a new such customer buys one such book, suggest the others too. ? Associations may be used as a first step in detec
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