Mining object spatial multimedia and text
These are complex types of data. If the database is object-relational database or object oriented database, then they can be mined by using generalization and assigning classes to these complex objects including set based, list, inheritance and composition based hierarchies. They can also be mined by visualizing them as object data cubes. Finally, they can be used with generalization based mining.
Spatial data mining finds interesting patterns from large geospatial databases Spatial data cubes are constructed using spatial dimensions and measures. These can be queried using spatial OLAP . Spatial mining includes mining spatial association and collocation patterns, clustering, classification and special trend and outlier analysis.
Multimedia data mining finds interesting patterns from multimedia databases which store audio data, image data, video data, sequence data, and hypertext data containing text, markups and linkages. Mining involves finding patterns based on content and similarity measures, generalization and multidimensional analysis. Mining also involves classification and prediction, mining associations and audio and video data mining.
Text or document database mining uses precision, recall and F-score to measure the effectiveness of mining. As discussed earlier, various text retrieval methods have been developed where the queries can specify constraints on the documents to select or the documents have a ranking that enables a selection. As an example, if we use similarity measures between keywords, then the documents can be ranked in the order of relevance. Text that has a lot of attributes can be reduced with indexes such as in Latex Semantic Indexing (LSI), Locality preserving Indexing (LPI), and probabilistic LSI. Text mining is not limited to keyword based and similarity based search. It could involve key-board based associations, document classification and document clustering.
Web mining looks for web linkage structures, web contents and web access patterns. Web page layouts, web link structures, associated multimedia data and classification of web pages are all used in this mining.
These are complex types of data. If the database is object-relational database or object oriented database, then they can be mined by using generalization and assigning classes to these complex objects including set based, list, inheritance and composition based hierarchies. They can also be mined by visualizing them as object data cubes. Finally, they can be used with generalization based mining.
Spatial data mining finds interesting patterns from large geospatial databases Spatial data cubes are constructed using spatial dimensions and measures. These can be queried using spatial OLAP . Spatial mining includes mining spatial association and collocation patterns, clustering, classification and special trend and outlier analysis.
Multimedia data mining finds interesting patterns from multimedia databases which store audio data, image data, video data, sequence data, and hypertext data containing text, markups and linkages. Mining involves finding patterns based on content and similarity measures, generalization and multidimensional analysis. Mining also involves classification and prediction, mining associations and audio and video data mining.
Text or document database mining uses precision, recall and F-score to measure the effectiveness of mining. As discussed earlier, various text retrieval methods have been developed where the queries can specify constraints on the documents to select or the documents have a ranking that enables a selection. As an example, if we use similarity measures between keywords, then the documents can be ranked in the order of relevance. Text that has a lot of attributes can be reduced with indexes such as in Latex Semantic Indexing (LSI), Locality preserving Indexing (LPI), and probabilistic LSI. Text mining is not limited to keyword based and similarity based search. It could involve key-board based associations, document classification and document clustering.
Web mining looks for web linkage structures, web contents and web access patterns. Web page layouts, web link structures, associated multimedia data and classification of web pages are all used in this mining.
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