Multi-Relational Concept Discovery with Aggregation

Date

2009-09-16

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

105
views

0
downloads

Concept discovery aims at finding the rules that best describe the given target predicate (i.e., the concept). Aggregation information such as average, count, max, etc. are descriptive for the domains that an aggregated value takes part in the definition of the concept. Therefore, a concept discovery system needs aggregation capability in order to construct high quality rules (with high accuracy and coverage) for such domains. In this work, we describe a method for concept discovery with aggregation on an ILP-based concept discovery system, namely C(2)D-A. C(2)D-A extends C(2)D by considering all instances together and thus improves the generated rule's quality. Together with this extension, aggregation handling mechanism is modified accordingly, leading to more accurate aggregate values, as well.

URI

https://hdl.handle.net/11511/53743

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Analyzing Transitive Rules on a Hybrid Concept Discovery System Kavurucu, Yusuf; Karagöz, Pınar; Toroslu, İsmail Hakkı (2009-06-12) Multi-relational concept discovery anus to find the relational rules that best describe the target concept. An important challenge that relational knowledge discovery systems face is intractably large search space and there is a trade-off between printing the search space for fast discovery and generating high quality rules. Combining ILP approach with conventional association rule mining techniques provides effective printing mechanisms. Due to the nature of Apriori algorithm, the facts that do not have co...
Time Constrained Temporal Logic Control of Multi Affine Systems Aydın Göl, Ebru (2012-01-01) In this paper, we consider the problem of controlling a dynamical system such that its trajectories satisfy a temporal logic property in a given amount of time. We focus on multiaffine systems and specifications given as syntactically co-safe linear temporal logic formulas over rectangular regions in the state space. The proposed algorithm is based on the estimation of time bounds for facet reachability problems and solving a time optimal reachability problem on the product between a weighted transition sys...
Linearization-based attitude error regulation: multiplicative error case Doruk, R. Ozgur (2009-01-01) Purpose - The purpose of this paper is to design and simulate a linearized attitude stabilizer based on linear quadratic regulator theory (LQR) using the multiplicative definition of the attitude.
Time-constrained temporal logic control of multi-affine systems Aydın Göl, Ebru (Elsevier BV, 2013-11-01) In this paper, we consider the problem of controlling a dynamical system such that its trajectories satisfy a temporal logic property in a given amount of time. We focus on multi-affine systems and specifications given as syntactically co-safe linear temporal logic formulas over rectangular regions in the state space. The proposed algorithm is based on estimating the time bounds for facet reachability problems and solving a time optimal reachability problem on the product between a weighted transition syste...
Improving scalability and efficiency of ILP-based and graph-based concept discovery systems Mutlu, Alev; Karagöz, Pınar; Department of Computer Engineering (2013) Concept discovery is the problem of finding definitions of target relation in terms or other relation given as a background knowledge. Inductive Logic Programming (ILP)-based and graph-based approaches are two competitors in concept discovery problem. Although ILP-based systems have long dominated the area, graph-based systems have recently gained popularity as they overcome certain shortcomings of ILP-based systems. While having applications in numerous domains, ILP-based concept discovery systems still su...

Citation Formats

Y. Kavurucu, P. Karagöz, and İ. H. Toroslu, “Multi-Relational Concept Discovery with Aggregation,” 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53743.