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Title: Data mining and database systems : integrating conceptual clustering with a relational database management system
Author: Lepinioti, Konstantina.
ISNI:       0000 0004 2699 543X
Awarding Body: Bournemouth University
Current Institution: Bournemouth University
Date of Award: 2011
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Many clustering algorithms have been developed and improved over the years to cater for large scale data clustering. However, much of this work has been in developing numeric based algorithms that use efficient summarisations to scale to large data sets. There is a growing need for scalable categorical clustering algorithms as, although numeric based algorithms can be adapted to categorical data, they do not always produce good results. This thesis presents a categorical conceptual clustering algorithm that can scale to large data sets using appropriate data summarisations. Data mining is distinguished from machine learning by the use of larger data sets that are often stored in database management systems (DBMSs). Many clustering algorithms require data to be extracted from the DBMS and reformatted for input to the algorithm. This thesis presents an approach that integrates conceptual clustering with a DBMS. The presented approach makes the algorithm main memory independent and supports on-line data mining.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Computer Science and Informatics