\u00a0Issues<\/span><\/h3>\n\n\n- Huge amount of data<\/li>\n
- Most algorithms exploit ad-hoc main memory data structures to efficiently perform data mining\n
\n- These approaches rely on the available physical memory and may run out of memory when the analysis is performed on very large databases<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
Goal<\/h4>\n<\/div>\n\n\n- design of disk-based structures and algorithms to efficiently support clustering algorithms<\/li>\n
- data mining optimizer to automatically select appropriate\u00a0 algorithms and data access methods for frequent\u00a0 itemset mining<\/li>\n
- text mining by exploiting data mining techniques\u00a0\u00a0 (e.g., clustering, association rules) in different application domain<\/li>\n
- study of parallel and distributed algorithms to scale itemset mining<\/li>\n<\/ul>\n<\/div>\n\n
<\/h3>\nGeneralized association rule mining with constraints<\/h3>\nTutor<\/h4>\n
Luca Cagliero<\/a>, Tania Cerquitelli<\/a> (email: name dot surname at polito dot it)<\/p>\n <\/p>\n
Description<\/h4>\n\n- Generalized association rule mining: extension of the traditional association rule mining problem in the presence of taxonomies (aggregation hierarchies) built over data items\n
\n- Discovery of correlations among data at different abstraction levels<\/li>\n
- High number of mined rules -> high computational complexity<\/li>\n<\/ul>\n<\/li>\n
- Constraints restrict the extracted rules to a subset of interest to ease domain expert analysis<\/li>\n<\/ul>\n
Applications:<\/em><\/p>\n\n- Study and implementation of novel generalized association rule mining algorithms with constraints<\/li>\n
- Design of applications, based on generalized rule mining with constraints, to support knowledge discovery data coming different application contexts (e.g., network traffic data, mobile data, social network data)<\/li>\n<\/ul>\n
<\/h3>\n
<\/p>\n
An optimizer to support data mining activities \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0<\/span><\/h3>\n![\"\"](\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2011\/11\/opt.png\")
<\/h3>\nTutor<\/h4>\nTania Cerquitelli<\/a>\nDescription<\/h4>\n\n- \u00a0Association rule extraction\n
\n- Frequent itemset extraction -> computationally intensive<\/li>\n
- Association rule generation from frequent itemsets<\/li>\n<\/ul>\n<\/li>\n
- Research activity usually focuses on defining efficient algorithms for itemset extraction<\/li>\n
- Different algorithms are suitable for different data distribution<\/li>\n
- Some algorithms have been integrated into a DBMS Open Source kernel<\/li>\n<\/ul>\n
Applications:<\/em><\/p>\n\n- Data mining optimizer to automatically select appropriate algorithms and data access methods for frequent itemset mining<\/li>\n
- Design and develop a module (i.e., an optimizer), in case integrated into a DBMS Open Source kernel (e.g., PostgreSQL), which is able to select for each mining process the best algorithm for the current data distribution<\/li>\n<\/ul>\n
<\/p>\n
","protected":false},"excerpt":{"rendered":"
Disk-based algorithms to scale-up data mining task Tutors , \u00a0Issues Huge amount of data Most algorithms exploit ad-hoc main memory data structures to efficiently perform data mining These approaches rely on the available physical memory and may run out of memory when the analysis is performed on very large databases Goal design of disk-based structures[…]<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":2369,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2608","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/2608","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\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/comments?post=2608"}],"version-history":[{"count":37,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/2608\/revisions"}],"predecessor-version":[{"id":8784,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/2608\/revisions\/8784"}],"up":[{"embeddable":true,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/2369"}],"wp:attachment":[{"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/media?parent=2608"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- \n
- Huge amount of data<\/li>\n
- Most algorithms exploit ad-hoc main memory data structures to efficiently perform data mining\n
- \n
- These approaches rely on the available physical memory and may run out of memory when the analysis is performed on very large databases<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
Goal<\/h4>\n<\/div>\n
\n- \n
- design of disk-based structures and algorithms to efficiently support clustering algorithms<\/li>\n
- data mining optimizer to automatically select appropriate\u00a0 algorithms and data access methods for frequent\u00a0 itemset mining<\/li>\n
- text mining by exploiting data mining techniques\u00a0\u00a0 (e.g., clustering, association rules) in different application domain<\/li>\n
- study of parallel and distributed algorithms to scale itemset mining<\/li>\n<\/ul>\n<\/div>\n\n
<\/h3>\n
Generalized association rule mining with constraints<\/h3>\n
Tutor<\/h4>\n
Luca Cagliero<\/a>, Tania Cerquitelli<\/a> (email: name dot surname at polito dot it)<\/p>\n
<\/p>\n
Description<\/h4>\n
- \n
- Generalized association rule mining: extension of the traditional association rule mining problem in the presence of taxonomies (aggregation hierarchies) built over data items\n
- \n
- Discovery of correlations among data at different abstraction levels<\/li>\n
- High number of mined rules -> high computational complexity<\/li>\n<\/ul>\n<\/li>\n
- Constraints restrict the extracted rules to a subset of interest to ease domain expert analysis<\/li>\n<\/ul>\n
Applications:<\/em><\/p>\n
- \n
- Study and implementation of novel generalized association rule mining algorithms with constraints<\/li>\n
- Design of applications, based on generalized rule mining with constraints, to support knowledge discovery data coming different application contexts (e.g., network traffic data, mobile data, social network data)<\/li>\n<\/ul>\n
<\/h3>\n
<\/p>\n
An optimizer to support data mining activities \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0<\/span><\/h3>\n
<\/h3>\nTutor<\/h4>\nTania Cerquitelli<\/a>\n
Description<\/h4>\n
- \n
- \u00a0Association rule extraction\n
- \n
- Frequent itemset extraction -> computationally intensive<\/li>\n
- Association rule generation from frequent itemsets<\/li>\n<\/ul>\n<\/li>\n
- Research activity usually focuses on defining efficient algorithms for itemset extraction<\/li>\n
- Different algorithms are suitable for different data distribution<\/li>\n
- Some algorithms have been integrated into a DBMS Open Source kernel<\/li>\n<\/ul>\n
Applications:<\/em><\/p>\n
- \n
- Data mining optimizer to automatically select appropriate algorithms and data access methods for frequent itemset mining<\/li>\n
- Design and develop a module (i.e., an optimizer), in case integrated into a DBMS Open Source kernel (e.g., PostgreSQL), which is able to select for each mining process the best algorithm for the current data distribution<\/li>\n<\/ul>\n
<\/p>\n
","protected":false},"excerpt":{"rendered":"Disk-based algorithms to scale-up data mining task Tutors , \u00a0Issues Huge amount of data Most algorithms exploit ad-hoc main memory data structures to efficiently perform data mining These approaches rely on the available physical memory and may run out of memory when the analysis is performed on very large databases Goal design of disk-based structures[…]<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":2369,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2608","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/2608","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\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/comments?post=2608"}],"version-history":[{"count":37,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/2608\/revisions"}],"predecessor-version":[{"id":8784,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/2608\/revisions\/8784"}],"up":[{"embeddable":true,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/2369"}],"wp:attachment":[{"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/media?parent=2608"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- \u00a0Association rule extraction\n
- Generalized association rule mining: extension of the traditional association rule mining problem in the presence of taxonomies (aggregation hierarchies) built over data items\n
- These approaches rely on the available physical memory and may run out of memory when the analysis is performed on very large databases<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n