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One approach to this problem is as follows To nd people with similar past preferences we create clusters of people based on their preferences for movies The accuracy of clustering can be improved by previously clustering movies by their similarity, so even if people have not seen the same movies, if they have seen similar movies they would be clustered together We can repeat the clustering, alternately clustering people, then movies, then people, and so on till we reache an equilibrium Given a new user, we nd a cluster of users most similar to that user, on the basis of the user s preferences for movies already seen We then predict movies in movie clusters that are popular with that user s cluster as likely to be interesting to the new user In fact, this problem is an instance of collaborative ltering, where users collaborate in the task of ltering information to nd information of interest
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Text mining applies data mining techniques to textual documents For instance, there are tools that form clusters on pages that a user has visited; this helps users when they browse the history of their browsing to nd pages they have visited earlier The distance between pages can be based, for instance, on common words in the pages (see Section 22513) Another application is to classify pages into a Web directory automatically, according to their similarity with other pages (see Section 2255) Data-visualization systems help users to examine large volumes of data, and to detect patterns visually Visual displays of data such as maps, charts, and other graphical representations allow data to be presented compactly to users A single graphical screen can encode as much information as a far larger number of text screens For example, if the user wants to nd out whether production problems at plants are correlated to the locations of the plants, the problem locations can be encoded in a special color say, red on a map The user can then quickly discover locations where problems are occurring The user may then form hypotheses about why problems are occurring in those locations, and may verify the hypotheses quantitatively against the database As another example, information about values can be encoded as a color, and can be displayed with as little as one pixel of screen area To detect associations between pairs of items, we can use a two-dimensional pixel matrix, with each row and each column representing an item The percentage of transactions that buy both items can be encoded by the color intensity of the pixel Items with high association will show up as bright pixels in the screen easy to detect against the darker background Data visualization systems do not automatically detect patterns, but provide system support for users to detect patterns Since humans are very good at detecting visual patterns, data visualization is an important component of data mining
224 Data Warehousing
Large companies have presences in many places, each of which may generate a large volume of data For instance, large retail chains have hundreds or thousands of stores, whereas insurance companies may have data from thousands of local branches Further, large organizations have a complex internal organization structure, and there-
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fore different data may be present in different locations, or on different operational systems, or under different schemas For instance, manufacturing-problem data and customer-complaint data may be stored on different database systems Corporate decision makers require access to information from all such sources Setting up queries on individual sources is both cumbersome and inef cient Moreover, the sources of data may store only current data, whereas decision makers may need access to past data as well; for instance, information about how purchase patterns have changed in the past year could be of great importance Data warehouses provide a solution to these problems A data warehouse is a repository (or archive) of information gathered from multiple sources, stored under a uni ed schema, at a single site Once gathered, the data are stored for a long time, permitting access to historical data Thus, data warehouses provide the user a single consolidated interface to data, making decision-support queries easier to write Moreover, by accessing information for decision support from a data warehouse, the decision maker ensures that online transaction-processing systems are not affected by the decision-support workload
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