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rain forest is a scalable algorithm for decision tree induction develop a scalable naumlive bayesian classification
design an efficient method that performs effective natiive bayesian classification over an infinite data stream ie you
it is important to calculate the worst-case computational complexity of the decision tree algorithm given data set d
why is tree pruning useful in decision tree inductionwhat is a drawback of using a separate set of tuples to evaluate
it is interesting to generate semantic annotations for mined patterns section 761 presented a pattern annotation method
association rule mining often generates a large number of rules many of which may be similar thus not containing much
frequent pattern mining may generate many superfluous patterns therefore it is important to develop methods that mine
the price of each item in a store is non-negative the store manager is only interested in rules of certain forms using
section 724 presented various ways of defining negatively correlated patterns consider definition 73 suppose that item
in multidimensional data analysis it is interesting to extract pairs of similar cell characteristics associated with
quantitative association rules may disclose exceptional behaviors within a data set where exceptional can be defined
semi-supervised classification active learning and transfer learning are useful for situations in which unlabeled data
for the k-means algorithm it is interesting to note that by choosing the initial cluster centers carefully we may be
the support vector machine is a highly accurate classification method however svm classifiers suffer from slow
the following table consists of training data from an employee database the data have been generalized for example 31
outline methods for addressing the class imbalance problem suppose a bank wants to develop a classifier that guards
the data tuples of figure 825 are sorted by decreasing probability value as returned by a classifier for each tuple
suppose that you are to allocate a number of automatic teller machines atms in a given region so as to satisfy a number
traditional clustering methods are rigid in that they require each object to belong exclusively to only one cluster
human eyes are fast and effective at judging the quality of clustering methods for 2-d data can you design a data
give an example of how specific clustering methods can be integrated for example where one clustering algorithm is used
for constraint-based clustering aside from having the minimum number of customers in each cluster for atm allocation as
why is it that birch encounters difficulties in finding clusters of arbitrary shape but optics does not propose
compare the ma ple algorithm section 1123 with the frequent closed item set mining algorithm closet pei han and mao
consider the nested loop approach to mining distance-based outliers figure 126 suppose the objects in a data set are