Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based Reasoning

https://doi.org/10.22146/ijccs.30423

Damar Riyadi(1*), Aina Musdholifah(2)

(1) Master of Computer Science, FMIPA UGM, Yogyakarta
(2) Department of Electronics and Computer Science, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


This study aims to improve the performance of Case-Based Reasoning by utilizing cluster analysis which is used as an indexing method to speed up case retrieval in CBR. The clustering method uses Local Triangular Kernel-based Clustering (LTKC). The cosine coefficient method is used for finding the relevant cluster while similarity value is calculated using Manhattan distance, Euclidean distance, and Minkowski distance. Results of those methods will be compared to find which method gives the best result. This study uses three test data: malnutrition disease, heart disease, and thyroid disease. Test results showed that CBR with LTKC-indexing has better accuracy and processing time than CBR without indexing. The best accuracy on threshold 0.9 of malnutrition disease, obtained using the Euclidean distance which produces 100% accuracy and 0.0722 seconds average retrieval time. The best accuracy on threshold 0.9 of heart disease, obtained using the Minkowski distance which produces 95% accuracy and 0.1785 seconds average retrieval time. The best accuracy on threshold 0.9 of thyroid disease, obtained using the Minkowski distance which produces 92.52% accuracy and 0.3045 average retrieval time. The accuracy comparison of CBR with SOM-indexing, DBSCAN-indexing, and LTKC-indexing for malnutrition diseases and heart disease resulted that they have almost equal accuracy.


Keywords


case-based reasoning; indexing, clustering; LTKC; nearest neighbor retrieval

Full Text:

PDF


References

[1]   R. Weber, Case-Based Reasoning: A Text Book. Berlin: Springer-Verlag, 2013.

[2]   J. Shu, “The Application of Ant Colony Optimization in CBR,” Adv. Intell. Syst. Comput., vol. 212, 2013 [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-642-37502-6_143. [Accessed: 10-Nov-2017].

[3]   A. Sarkheyli and D. Söffker, “Case Indexing in Case-Based Reasoning by Applying Situation Operator Model as Knowledge Representation Model,” IFAC-PapersOnLine, vol. 28, no. 1, pp. 81–86, 2015 [Online]. Available: http://www.sciencedirect.com/science/article/pii/S240589631500049X. [Accessed: 10-Nov-2017].

[4]   I. H. Witten, Data Mining Practical Machine Learning Tools and Techniques, 4th ed., vol. 18 Suppl, no. 1. Cambridge, Massachusetts: Morgan Kaufmann, 2016.

[5]   A. Musdholifah, S. Zaiton, and M. Hashim, “Cluster Analysis on High-Dimensional Data : A Comparison of Density-based Clustering Algorithms,” vol. 7, no. 2, pp. 380–389, 2013 [Online]. Available: http://ajbasweb.com/old/ajbas/2013/February/380-389.pdf. [Accessed: 10-Nov-2017].

[6]   E. Faizal and S. Hartati, “Case-Based Reasoning untuk Mendiagnosa Penyakit Cardiovascular dengan Metode Weighted Minkowski,” S2 Ilmu Komputer, Universitas Gadjah Mada, 2013.

[7]   E. Wahyudi and S. Hartati, “Case-Based Reasoning untuk Diagnosis Penyakit Jantung,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 11, no. 1, p. 1, Jan. 2017 [Online]. Available: https://journal.ugm.ac.id/ijccs/article/view/15523. [Accessed: 21-Mar-2017]

[8]   Nurfalinda, S. Hartati, and A. Musdholifah, “Case Based Reasoning dan Rule Based Reasoning untuk Diagnosis Penyakit Gizi Buruk pada Balita,” S2 Ilmu Komputer, Universitas Gadjah Mada, 2016.

[9]   T. Rismawan and S. Hartati, “Case-Based Reasoning untuk Diagnosa Penyakit THT (Telinga Hidung dan Tenggorokan),” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 6, no. 2, 2013 [Online]. Available: https://jurnal.ugm.ac.id/ijccs/article/view/2154. [Accessed: 09-May-2017]

[10] B. Desgraupes, “Clustering Indices,” CRAN Packag., no. April, pp. 1–10, 2013 [Online]. Available: cran.r-project.org/web/packages/clusterCrit. [Accessed: 10-Nov-2017].

[11] H. Santoso, “Metode Indexing pada Case Based Reasoning (CBR) menggunakan Density Based Spatial Clustering Application with Noise (DBSCAN),” S2 Ilmu Komputer, Universitas Gadjah Mada, 2017.

[12] A. M. Salem, M. Roushdy, and R. A. Hodhod, “A Case Based Expert System for Supporting Diagnosis of Heart Diseases,” Heart Fail., no. 5, pp. 33–39, 2005 [Online]. Available: http://www.cs.ru.ac.za/courses/honours/ai/casebaseddiagnosis.pdf. [Accessed: 10-Nov-2017].

[13] H. Núñez et al., “A Comparative Study on The Use of Similarity Measures in Case-Based Reasoning to Improve The Classification of Environmental System Situations,” Environ. Model. Softw., vol. 19, no. 9, pp. 809–819, 2004 [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1364815203002020. [Accessed: 10-Nov-2017].



DOI: https://doi.org/10.22146/ijccs.30423

Article Metrics

Abstract views : 965 | views : 873

Refbacks

  • There are currently no refbacks.




Copyright (c) 2018 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

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



Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs



View My Stats1
View My Stats2