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in the density-based outlier detection method of section 1243 the definition of local reachability density has a
because clusters may form a hierarchy outliers may belong to different granularity levels propose a clustering-based
to understand why angle-based outlier detection is a heuristic method give an example where it does not work well can
many studies analyze homogeneous information networks eg social networks consisting of friends linked with friends
research and describe a data mining application that was not presented in this chapter discuss how different forms of
why is the establishment of theoretical foundations important for data mining name and describe the main theoretical
research project building a theory of data mining requires setting up a theoretical framework so that the major data
there is a strong linkage between statistical data analysis and data mining some people think of data mining as
give an application example where global outliers contextual outliers and collective outliers are all interesting what
consider partitioning clustering and the following constraint on clusters the number of objects in each cluster must be
in a large sparse graph where on average each node has a low degree is the similarity matrix using sim rank still
suppose item i appears exactly s times in a file of n baskets where s is the support threshold if we take a sample of
suppose we are counting frequent item sets in a decaying window with a decay constant c suppose also that with
here is a collection of twelve baskets each contains three of the six items 1 through 6suppose the support threshold is
suppose we have market baskets that satisfy the following assumptions1 the support threshold is 100002 there are one
suppose the support threshold is 5 find the maximal frequent item sets for the data ofa exercise 611b exercise
how would you count all item sets of size 3 by a generalization of the triangular-matrix method that is arrange that in
let there be i items in a market-basket data set of b baskets suppose that every basket contains exactly k items as a
a popular example of the design of an on-line algorithm to minimize the competitive ratio is the ski-buying problem 3
for the three clusters of fig 78a compute the representation of the cluster as in the bfr algorithm that is compute n
suppose a cluster of three-dimensional points has standard deviations of 2 3 and 5 in the three dimensions in that
compute the radius in the sense used by the grgpf algorithm square root of the average square of the distance from the
compute the density of each of the three clusters in fig 72 if density is defined to be the number of points divided
perform a hierarchical clustering of the one-dimensional set of points 1 4 9 16 25 36 49 64 81 assuming clusters are
if we wish to start out as in fig 910 with all u and v entries set to the same value what value minimizes the rmse for