A Path-Finding Based Method for Concept Discovery in Graphs

Date

2015-07-08

Author

Abay, Nazmiye Ceren
Mutlu, Alev
Karagöz, Pınar

Metadata

Show full item record

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

Item Usage Stats

124
views

0
downloads

In the multi-relational data mining, concept discovery is the problem of inducing definitions of a relation in terms of other relations provided. In this paper, we present a method that combines graph-based and association rule mining-based methods for concept discovery in graphs. The proposed method is related to graphs as the data, which is initially stored in a relational database, is represented as a graph and concept descriptors are the paths that connect certain vertices; and it is related to association rule mining as it uses methods of association rule mining to prune the search space and evaluate the quality of the concept descriptors. The method is evaluated on several data sets, and the experimental results show that it is compatible with the state-of-the art methods in terms of accuracy and coverage of the induced concept descriptors and the running time of the application.

URI

https://hdl.handle.net/11511/55407

Conference Name

6th International Conference on Information, Intelligence, Systems and Applications (IISA)

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

A Graph-Based Concept Discovery Method for n-Ary Relations Abay, Nazmiye Ceren; MUTLU, ALEV; Karagöz, Pınar (2015-09-04) Concept discovery is a multi-relational data mining task for inducing definitions of a specific relation in terms of other relations in the data set. Such learning tasks usually have to deal with large search spaces and hence have efficiency and scalability issues. In this paper, we present a hybrid approach that combines association rule mining methods and graph-based approaches to cope with these issues. The proposed method inputs the data in relational format, converts it into a graph representation, and...
A Counting-Based Heuristic for ILP-Based Concept Discovery Systems Mutlu, Alev; Karagöz, Pınar; Kavurucu, Yusuf (2013-09-13) Concept discovery systems are concerned with learning definitions of a specific relation in terms of other relations provided as background knowledge. Although such systems have a history of more than 20 years and successful applications in various domains, they are still vulnerable to scalability and efficiency issues - mainly due to large search spaces they build. In this study we propose a heuristic to select a target instance that will lead to smaller search space without sacrificing the accuracy. The p...
A receptance based method for the calculation of nonlinear normal modes of large ordered structures with distributed localized nonlinearities Samandari, Hamed; Ciğeroğlu, Ender (2022-12-01) In recent years, the concept of nonlinear normal modes (NNMs) has gained interest for interpreting a broad range of nonlinear dynamic phenomena. Present study introduces a simple and efficient computational framework to compute NNMs of large order nonlinear structures overcoming difficulties associated with commonly used time-integration and discretization methods. In this paper, a Receptance Based Nonlinear Normal Mode Calculation Method (RBNM) is developed to determine the NNMs of large ordered realistic ...
A SPINOR MODEL FOR QUANTUM COSMOLOGY DERELI, T; ONDER, M; TUCKER, RW (1994-03-31) The question of the interpretation of Wheeler-DeWitt solutions in the context of cosmological models is addressed by implementing the Hamiltonian constraint as a spinor wave equation in minisuperspace. We offer a relative probability interpretation based on a non-closed vector current in this space and a prescription for a parametrisation of classical solutions in terms of classical time. Such a prescription can accommodate classically degenerate metrics describing manifolds with signature change. The relat...
A generative model for multi class object recognition and detection Ulusoy, İlkay (2006-01-01) In this study, a generative type probabilistic model is proposed for object recognition. This model is trained by weakly labelled images and performs classification and detection at the same time. When test on highly challenging data sets, the model performs good for both tasks (classification and detection).

Citation Formats

N. C. Abay, A. Mutlu, and P. Karagöz, “A Path-Finding Based Method for Concept Discovery in Graphs,” presented at the 6th International Conference on Information, Intelligence, Systems and Applications (IISA), Corfu, GREECE, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55407.