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【正文】 Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Figure 169。ve algorithm 1. Consider all possible sets of relevant items. 2. For each set find its support (., count how many transactions purchase all items in the set). ? Large itemsets: sets with sufficiently high support 3. Use large itemsets to generate association rules. 1. From itemset A generate the rule A {b } ?b for each b ? A. ? Support of rule = support (A). ? Confidence of rule = support (A ) / support (A {b }) 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Na239。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Best Splits (Cont.) ? Measure of “cost” of a split: Informationcontent (S, {S1, S2, ….., Sr})) = – ? ? Informationgain ratio = Informationgain (S, {S1, S2, ……, Sr}) Informationcontent (S, {S1, S2, ….., Sr}) ? The best split is the one that gives the maximum information gain ratio log2 r i 1 |Si| |S| |Si| |S| 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Data Warehouse Schema 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Chapter 20: Data Analysis ? Decision Support Systems ? Data Warehousing ? Data Mining ? Classification ? Association Rules ? Clustering 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Design Issues ? When and how to gather data ? Source driven architecture: data sources transmit new information to warehouse, either continuously or periodically (., at night) ? Destination driven architecture: warehouse periodically requests new information from data sources ? Keeping warehouse exactly synchronized with data sources (., using twophase mit) is too expensive ? Usually OK to have slightly outofdate data at warehouse ? Data/updates are periodically downloaded form online transaction processing (OLTP) systems. ? What schema to use ? Schema integration 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Construction of Decision Trees ? Training set: a data sample in which the classification is already known. ? Greedy top down generation of decision trees. ? Each internal node of the tree partitions the data into groups based on a partitioning attribute, and a partitioning condition for the node ? Leaf node: ? all (or most) of the items at the node belong to the same class, or ? all attributes have been considered, and no further partitioning is possible. 169。 Use best split found (across all attributes) to partition S into S1, S2, …., S r, for i = 1, 2, ….., r Partition (Si )。 the population consists of a set of instances ? ., each transaction (sale) at a shop is an instance, and the set of all transactions is the population 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Clustering Algorithms ? Clustering algorithms have been designed to handle very large datasets ? ., the Birch algorithm ? Main idea: use an inmemory Rtree to store points that are being clustered ? Insert points one at a time into the Rtree, merging a new point with an existing cluster if is less than some ? distance away ? If there are more leaf nodes than fit in memory, merge existing clusters that are close to each other ? At the end of first pass we get a large number of clusters at the leaves of the Rtree ? Merge clusters to reduce the number of clusters 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Figure 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Clustering ? Clustering: Intuitively, finding clusters of points in the given data such that similar points lie in the same cluster ? Can
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