Mining Multiple Related Tables Using Object-Oriented Model

An object-oriented database is represented by a set of classes connected by their class inheritance hierarchy through superclass and subclass relationships. An object-oriented database is suitable for capturing more details and complexity for real world data. Existing algorithms for mining multiple databases are either Apriori-based or machine learning techniques, but are not suitable for mining multiple object-oriented databases. This thesis proposes an object-oriented class model and database schema, and a series of class methods including that for object-oriented join ( OOJoin) which joins superclass and subclass tables by matching their type and super type relationships, mining Hierarchical Frequent Patterns ( MineHFPs) from multiple integrated databases by applying an extended TidFP technique which specifies the class hierarchy by traversing the multiple database inheritance hierarchy. This thesis also extends map-gen join method used in TidFP algorithm to oomap-gen join for generating k-itemset candidate pattern to reduce the candidate itemset generation by indexing the (k-1)-itemset candidate pattern using two position codes of start position and end position codes tied to inheritance hierarchy level. Experiments show that the proposed MineHFPs algorithm for mining hierarchical frequent patterns is more effective and efficient for complex queries

Enhancing web marketing by using ontology

The existence of the Web has a major impact on people\u27s life styles. Online shopping, online banking, email, instant messenger services, search engines and bulletin boards have gradually become parts of our daily life. All kinds of information can be found on the Web. Web marketing is one of the ways to make use of online information. By extracting demographic information and interest information from the Web, marketing knowledge can be augmented by applying data mining algorithms. Therefore, this knowledge which connects customers to products can be used for marketing purposes and for targeting existing and potential customers. The Web Marketing Project with Ontology Support has the purpose to find and improve marketing knowledge. In the Web Marketing Project, association rules about marketing knowledge have been derived by applying data mining algorithms to existing Web users\u27 data. An ontology was used as a knowledge backbone to enhance data mining for marketing. The Raising Method was developed by taking advantage of the ontology. Data are preprocessed by Raising before being fed into data mining algorithms. Raising improves the quality of the set of mined association rules by increasing the average support value. Also, new rules have been discovered after applying Raising. This dissertation thoroughly describes the development and analysis of the Raising method. Moreover, a new structure, called Intersection Ontology, is introduced to represent customer groups on demand. Only needed customer nodes are created. Such an ontology is used to simplify the marketing knowledge representation. Finally, some additional ontology usages are mentioned. By integrating an ontology into Web marketing, the marketing process support has been greatly improved