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Silberschatz Korth Sudarshan: Database System Concepts, Fourth Edition
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Classi cation deals with predicting the class of test instances, by using attributes of the test instances, based on attributes of training instances, and the actual class of training instances Classi cation can be used, for instance, to predict credit-worthiness levels of new applicants, or to predict the performance of applicants to a university There are several types of classi ers, such as Decision-tree classi ers These perform classi cation by constructing a tree based on training instances with leaves having class labels The tree is traversed for each test instance to nd a leaf, and the class of the leaf is the predicted class Several techniques are available to construct decision trees, most of them based on greedy heuristics Bayesian classi ers are simpler to construct than decision-tree classi ers, and work better in the case of missing/null attribute values Association rules identify items that co-occur frequently, for instance, items that tend to be bought by the same customer Correlations look for deviations from expected levels of association Other types of data mining include clustering, text mining, and data visualization Data warehouses help gather and archive important operational data Warehouses are used for decision support and analysis on historical data, for instance to predict trends Data cleansing from input data sources is often a major task in data warehousing Warehouse schemas tend to be multidimensional, involving one or a few very large fact tables and several much smaller dimension tables Information retrieval systems are used to store and query textual data such as documents They use a simpler data model than do database systems, but provide more powerful querying capabilities within the restricted model Queries attempt to locate documents that are of interest by specifying, for example, sets of keywords The query that a user has in mind usually cannot be stated precisely; hence, information-retrieval systems order answers on the basis of potential relevance Relevance ranking makes use of several types of information, such as: Term frequency: how important each term is to each document Inverse document frequency Site popularity Page rank and hub/authority rank are two ways to assign importance to sites on the basis of links to the site Similarity of documents is used to retrieve documents similar to an example document Synonyms and homonyms complicate the task of information retrieval
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Silberschatz Korth Sudarshan: Database System Concepts, Fourth Edition
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22
Advanced Querying and Information Retrieval
Precision and recall are two measures of the effectiveness of an information retrieval system Directory structures are used to classify documents with other similar documents
Review Terms
Decision-support systems Statistical analysis Multidimensional data Measure attributes Dimension attributes Cross-tabulation Data cube Online analytical processing (OLAP) Pivoting Slicing and dicing Rollup and drill down Multidimensional OLAP (MOLAP) Relational OLAP (ROLAP) Hybrid OLAP (HOLAP) Extended aggregation Variance Standard deviation Correlation Regression Ranking functions Rank Dense rank Partition by Windowing Data mining Prediction Associations Classi cation Training data Test data Decision-tree classi ers Partitioning attribute Partitioning condition Purity Gini measure Entropy measure Information gain Information content Information gain ratio Continuous-valued attribute Categorical attribute Binary split Multiway split Over tting Bayesian classi ers Bayes theorem Naive Bayesian classi ers Regression Linear regression Curve tting Association rules Population Support Con dence Large itemsets Other types of associations Clustering Hierarchical clustering Agglomerative clustering Divisive clustering Text mining Data visualization Data warehousing Gathering data Source-driven architecture
Silberschatz Korth Sudarshan: Database System Concepts, Fourth Edition
VII Other Topics
22 Advanced Querying and Information Retrieval
The McGraw Hill Companies, 2001
Exercises
Destination-driven architecture Data cleansing Merge purge Householding Warehouse schemas Fact table Dimension tables Star schema Information retrieval systems Keyword search Full text retrieval Term Relevance ranking Term frequency Inverse document frequency Relevance Proximity
Stop words Relevance using hyperlinks Site popularity Page rank Hub/authority ranking Similarity-based retrieval Synonyms Homonyms Inverted index False drop False negative False positive Precision Recall Web crawlers Directories Classi cation hierarchy
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