02-07-2010, 08:03 PM
05-07-2010, 10:35 AM
Me too... PLEASE DO POST SOMETHING EASY AND INTRESTING..
07-07-2010, 12:13 AM
i got a good topic on this while searching,but dont know much about that...
anyway the topic is"Integrating K-Means clustering With RDBMS" which is a research paper but very interesting and beneficial..
so please help by adding relevent information or links with this topic.
anyway the topic is"Integrating K-Means clustering With RDBMS" which is a research paper but very interesting and beneficial..
so please help by adding relevent information or links with this topic.
07-07-2010, 12:33 AM
read this
ABSTRACT
Integrating data mining algorithms with a relational DBMS is an important problem for database programmers. We introduce three SQL implementations of the popular K-means clustering algorithm to integrate it with a relational DBMS: 1) a straightforward translation of K-means computations into SQL, 2) an optimized version based on improved data organization, efficient indexing, sufficient statistics, and rewritten queries, and 3) an incremental version that uses the optimized version as a building block with fast convergence and automated reseeding. We experimentally show the proposed K-means implementations work correctly and can cluster large data sets. We identify which K-means computations are more critical for performance. The optimized and incremental K-means implementations exhibit linear scalability. We compare K-means implementations in SQL and C++ with respect to speed and scalability and we also study the time to export data sets outside of the DBMS. Experiments show that SQL overhead is significant for small data sets, but relatively low for large data sets, whereas export times become a bottleneck for C++
its ieee article so use this link to download the research article
http://doi.ieeecomputersociety10.1109/TKDE.2006.31
http://www.computerportal/web/csdl/doi/1...DE.2006.31
ABSTRACT
Integrating data mining algorithms with a relational DBMS is an important problem for database programmers. We introduce three SQL implementations of the popular K-means clustering algorithm to integrate it with a relational DBMS: 1) a straightforward translation of K-means computations into SQL, 2) an optimized version based on improved data organization, efficient indexing, sufficient statistics, and rewritten queries, and 3) an incremental version that uses the optimized version as a building block with fast convergence and automated reseeding. We experimentally show the proposed K-means implementations work correctly and can cluster large data sets. We identify which K-means computations are more critical for performance. The optimized and incremental K-means implementations exhibit linear scalability. We compare K-means implementations in SQL and C++ with respect to speed and scalability and we also study the time to export data sets outside of the DBMS. Experiments show that SQL overhead is significant for small data sets, but relatively low for large data sets, whereas export times become a bottleneck for C++
its ieee article so use this link to download the research article
http://doi.ieeecomputersociety10.1109/TKDE.2006.31
http://www.computerportal/web/csdl/doi/1...DE.2006.31
18-08-2010, 03:49 PM
one project based on "integrating k means clustering withRDBMS" is
"noise removal in database"
Is this a good one to develop as a project?
"noise removal in database"
Is this a good one to develop as a project?
14-02-2012, 10:44 AM
to get information about the topic data mining full report ,ppt and related topic refer the link bellow
https://seminarproject.net/Thread-data-m...ull-report
https://seminarproject.net/Thread-data-m...nar-report
https://seminarproject.net/Thread-data-m...ect-topics
https://seminarproject.net/Thread-data-m...a-proposal
https://seminarproject.net/Thread-data-m...ort?page=2
https://seminarproject.net/Thread-data-m...techniques
https://seminarproject.net/Thread-data-m...nformatics
https://seminarproject.net/Thread-data-m...eas?page=2
https://seminarproject.net/Thread-using-...test-suite
https://seminarproject.net/Thread-data-m...er-present
https://seminarproject.net/Thread-data-m...re-testing