{"id":4426,"date":"2012-11-29T09:36:51","date_gmt":"2012-11-29T08:36:51","guid":{"rendered":"http:\/\/dbdmg.polito.it\/wordpress\/?page_id=4426"},"modified":"2012-12-01T13:10:23","modified_gmt":"2012-12-01T12:10:23","slug":"strong-flipping-generalized-itemsets","status":"publish","type":"page","link":"https:\/\/dbdmg.polito.it\/wordpress\/strong-flipping-generalized-itemsets\/","title":{"rendered":"Strong Flipping Generalized Itemsets"},"content":{"rendered":"<h3><strong>OBJECTIVE<\/strong><\/h3>\n<p style=\"text-align: justify;\">Thanks to the rapid growth of social networks and online communities, large\u00a0social data collections are becoming more and more common, prompting the\u00a0need for scalable and innovative data analysis solutions. Several data analytics tools rely on data mining algorithms to gain interesting insights into\u00a0large data volumes. Generalized itemset mining is a well-known exploratory\u00a0data mining technique used to discover interesting high level data correlations. Since it allows e\ufb00ectively coping with sparse datasets, its application\u00a0to the user-generated content published on Twitter is an appealing research\u00a0issue. However, since patterns discovered at di\ufb00erent abstraction levels may\u00a0be in constrast in terms of correlation type (positive, negative, or null), their\u00a0manual inspection may become particularly interesting when a large number\u00a0of speci\ufb01c (descendant) itemsets show correlation type changes with respect\u00a0to their common ancestor.<\/p>\n<p style=\"text-align: justify;\">This work presents a novel data mining approach to e\ufb00ectively supporting Twitter data analysis by means of generalized itemsets. A novel kind of\u00a0patterns, namely the Strong Flipping Generalized Itemsets (SFGIs), is extracted from Twitter post content and contextual information supplied with\u00a0taxonomy hierarchies. Each SFGI is composed of a frequent generalized itemset X and the set of its descendants showing a correlation type change with\u00a0respect to X. Hence, SFGIs highlight contrasting situations in the analyzed\u00a0data, usually associated with interesting information.\u00a0An algorithm to mine\u00a0SFGIs at the top of the traditional generalized itemsets is also proposed.<\/p>\n<h3 align=\"LEFT\"><strong>EVALUATED REAL TWITTER DATASETS AND TAXONOMIES<\/strong><\/h3>\n<p>The\u00a0collection of evaluated Twitter datasets and the corresponding taxonomies is available <a href=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2012\/11\/twitterDatasets.zip\">here<\/a>.<\/p>\n<h3 align=\"LEFT\"><strong>SYNTHETIC DATA AND TAXONOMY GENERATOR<\/strong><\/h3>\n<p>The synthetic data generator is available <a href=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2012\/11\/IBMGenerator.zip\">here<\/a> (this is the Linux version of the standard IBM generator).\u00a0 Use the tax option to generate both data and taxonomy.<\/p>\n<br class=\"fixfloat\" \/>","protected":false},"excerpt":{"rendered":"<p>OBJECTIVE Thanks to the rapid growth of social networks and online communities, large\u00a0social data collections are becoming more and more common, prompting the\u00a0need for scalable and innovative data analysis solutions. Several data analytics tools rely on data mining algorithms to gain interesting insights into\u00a0large data volumes. Generalized itemset mining is a well-known exploratory\u00a0data mining technique<a href=\"https:\/\/dbdmg.polito.it\/wordpress\/strong-flipping-generalized-itemsets\/\">[&#8230;]<\/a><\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-4426","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/4426","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/comments?post=4426"}],"version-history":[{"count":14,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/4426\/revisions"}],"predecessor-version":[{"id":4431,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/4426\/revisions\/4431"}],"wp:attachment":[{"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/media?parent=4426"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}