ILP-based concept discovery in multi-relational data mining

Date

2009-11-01

Author

Kavurucu, Yusuf
Karagöz, Pınar
Toroslu, İsmail Hakkı

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

205
views

0
downloads

Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuristics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate high-quality patterns. In this work, an ILP-based concept discovery method, namely Confidence-based Concept Discovery (C(2)D), is described in which strong declarative biases and user-defined specifications are relaxed. Moreover, this new method directly works on relational databases. In addition to this, a new confidence-based pruning is used in this technique. We also describe how to define and use aggregate predicates as background knowledge in the proposed method. In order to use aggregate predicates, we show how to handle numerical attributes by using comparison operators on them. Finally, we analyze the effect of incorporating unrelated facts for generating transitive rules on the proposed method. A set of experiments are conducted on real-world problems to test the performance of the proposed method.

Subject Keywords

General Engineering, Artificial Intelligence, Computer Science Applications

URI

https://hdl.handle.net/11511/34730

Journal

EXPERT SYSTEMS WITH APPLICATIONS

DOI

https://doi.org/10.1016/j.eswa.2009.02.100

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Data mining in deductive databases using query flocks Toroslu, İsmail Hakkı (Elsevier BV, 2005-04-01) Data mining can be defined as a process for finding trends and patterns in large data. An important technique for extracting useful information, such as regularities, from usually historical data, is called as association rule mining. Most research on data mining is concentrated on traditional relational data model. On the other hand, the query flocks technique, which extends the concept of association rule mining with a 'generate-and-test' model for different kind of patterns, can also be applied to deduct...
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition Buyuksahin, Umit Cavus; Ertekin Bolelli, Şeyda (Elsevier BV, 2019-10-07) Many applications in different domains produce large amount of time series data. Making accurate forecasting is critical for many decision makers. Various time series forecasting methods exist that use linear and nonlinear models separately or combination of both. Studies show that combining of linear and nonlinear models can be effective to improve forecasting performance. However, some assumptions that those existing methods make, might restrict their performance in certain situations. We provide a new Au...
Concept discovery on relational databases: New techniques for search space pruning and rule quality improvement Kavurucu, Yusuf; Karagöz, Pınar; Toroslu, İsmail Hakkı (Elsevier BV, 2010-12-01) Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuristics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate high-quality patterns. In this work, we introduce an ILP-based c...
Confidence-based concept discovery in multi-relational data mining Kavurucu, Yusuf; Karagöz, Pınar; Toroslu, İsmail Hakkı (2008-03-21) Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuristics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate high-quality patterns. In this work, a new ILP-based concept dis...
Semantic information-based alternative plan generation for multiple query optimization Polat, Faruk; Alhajj, R (Elsevier BV, 2001-09-01) This paper addresses the impact of semantic information about queries on alternative plan generation (APG) for multiple query optimization (MQO). MQO covers optimizing the execution of a set of queries together where each query in the set to be optimized has several alternative execution plans. A multiple query optimizer selects an alternative plan for each query to obtain an optimal global execution plan. Our approach uses information such as common relations, common possible joins and common conditions to...

Citation Formats

Y. Kavurucu, P. Karagöz, and İ. H. Toroslu, “ILP-based concept discovery in multi-relational data mining,” EXPERT SYSTEMS WITH APPLICATIONS, pp. 11418–11428, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34730.