{"id":3700,"date":"2012-04-26T13:59:59","date_gmt":"2012-04-26T12:59:59","guid":{"rendered":"http:\/\/dbdmg.polito.it\/wordpress\/?page_id=3700"},"modified":"2014-11-13T10:25:54","modified_gmt":"2014-11-13T09:25:54","slug":"large-scale-itemset-mining","status":"publish","type":"page","link":"https:\/\/dbdmg.polito.it\/wordpress\/research\/large-scale-itemset-mining\/","title":{"rendered":"Large-scale itemset mining"},"content":{"rendered":"

Itemset mining focuses on the extraction of useful knowledge from huge quantities of data. A wide range of different domains need to deal with the ever-growing amounts of gathered data\u00a0 (e.g., biological data, network traffic data, text mining, streams of sensor network data, spatio-temporal data). Traditional in-core mining algorithms do not scale well with large volumes of data and are hindered by critical issues such as main-memory exhaustion and long execution times. Scalable and alternative approaches have to be devised to efficiently perform large-scale data mining. In this research activity, innovative approaches exploiting disk-based data structures and memory-efficient algorithms to extract frequent itemsets are investigated.<\/p>\n

Technical reports<\/h2>\n