Imine Index Support For Itemset Mining

Find all needed information about Imine Index Support For Itemset Mining. Below you can see links where you can find everything you want to know about Imine Index Support For Itemset Mining.


(PDF) IMine: Index Support for Item Set Mining

    https://www.researchgate.net/publication/220072411_IMine_Index_Support_for_Item_Set_Mining
    IMine: Index Support for Item Set Mining Elena Baralis, Tania Cerquitelli, and Silvia Chiusano Abstract —This paper presents the IMine index, a general and compact structure which provides tight...

IMine: Index Support for Item Set Mining - IEEE Journals ...

    https://ieeexplore.ieee.org/document/4609383/
    Aug 29, 2008 · Experiments, run for both sparse and dense data distributions, show the efficiency of the proposed index and its linear scalability also for large datasets. Item set mining supported by the IMine index shows performance always comparable with, and sometimes better than, state of the art algorithms accessing data on flat file.Cited by: 30

IMine: Index Support for Itemset Mining - CORE

    http://core.ac.uk/display/11403300
    The IMine index structure can be efficiently exploited by different item set extraction algorithms. In particular, IMine data access methods currently support the FP-growth and LCM v.2 algorithms, but they can straightforwardly support the enforcement of various constraint categories.

IMine: Index Support for Item Set Mining

    http://www.ijcttjournal.org/Volume1/Issue-3/IJCTT-V1I3P121.pdf
    support data mining queries. The IMine index (Item set-Mine index) is a novel data structure that provides a compact and complete representation of transactional data supporting efficient item set extraction from a relational DBMS. It is characterized by the following properties: The IMine index …

CiteSeerX — IMine: Index Support for Item Set Mining in ...

    http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.477.1812
    The Imine index method supports different item set extraction algorithms. Different rule mining algorithms are supported by Imine index scheme. At present the Imine index scheme is developed under PostgreSQL DBMS. The item set extraction and indexing operations …

IMine: Index Support for Item Set Mining

    https://www.infona.pl/resource/bwmeta1.element.ieee-art-000004609383
    Experiments, run for both sparse and dense data distributions, show the efficiency of the proposed index and its linear scalability also for large datasets. Item set mining supported by the IMine index shows performance always comparable with, and sometimes better than, state of …Cited by: 30

IMine: Index Support for Item Set Mining - CORE

    https://core.ac.uk/display/28520527
    IMine: Index Support for Item Set Mining . By T.SUNITHA. Abstract. This paper presents the IMine index, a general and compact structure which provides tight integration of item set extraction in a relational DBMS. Since no constraint is enforced during the index creation phase, IMine provides a complete representation of the original database. ...Author: T.SUNITHA

A new algorithm for fast mining frequent itemsets using N ...

    https://link.springer.com/article/10.1007/s11432-012-4638-z
    Jul 19, 2012 · Mining frequent itemsets has emerged as a fundamental problem in data mining and plays an essential role in many important data mining tasks. In this paper, we propose a novel vertical data representation called N-list, which originates from an FP-tree-like coding prefix tree called PPC-tree that stores crucial information about frequent itemsets.Cited by: 113

(PDF) Indexing Evolving Databases for Itemset Mining

    https://www.researchgate.net/publication/225698968_Indexing_Evolving_Databases_for_Itemset_Mining
    propose an index structure, called Itemset-Forest (I-F orest), for mining data modeled as sequences of incoming data blocks. The index supports user in- teraction, where the user specifies different...



Need to find Imine Index Support For Itemset Mining information?

To find needed information please read the text beloow. If you need to know more you can click on the links to visit sites with more detailed data.

Related Support Info