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CrossMine: Efficient Classification Across Multiple Database Relations (2004)
| Content Provider | CiteSeerX |
|---|---|
| Author | Yin, Xiaoxin Han, Jiawei Yang, Jiong Yu, Philip S. Watson, Ibm T. J. Ctr, Resch |
| Description | Most of today's structured data is stored in relational databases. Such a database consists of multiple relations which are linked together conceptually via entity-relationship links in the design of relational database schemas. Multi-relational classification can be widely used in many disciplines, such as financial decision making, medical research, and geographical applications. However, most classification approaches only work on single "flat" data relations. It is usually difficult to convert multiple relations into a single flat relation without either introducing huge, undesirable "universal relation" or losing essential information. Previous works using Inductive Logic Programming approaches (recently also known as Relational Mining) have proven effective with high accuracy in multi-relational classification. Unfortunately, they suffer from poor scalability w.r.t. the number of relations and the number of attributes in databases. |
| File Format | |
| Language | English |
| Publisher Date | 2004-01-01 |
| Publisher Institution | In Proc. 2004 Int. Conf. on Data Engineering (ICDE’04), Boston,MA |
| Access Restriction | Open |
| Subject Keyword | Multi-relational Classification Many Discipline Previous Work Essential Information Entity-relationship Link Relational Database Schema Poor Scalability Inductive Logic Programming Approach Single Flat Relation Geographical Application Medical Research Relational Mining Undesirable Universal Relation Multiple Relation Classification Approach Financial Decision Making Relational Database Single Flat Data Relation High Accuracy |
| Content Type | Text |
| Resource Type | Article |
