Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based Reasoning
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 re...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2018-07-01
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| Series: | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/ijccs/article/view/30423 |
| _version_ | 1819103842744139776 |
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| author | Damar Riyadi Aina Musdholifah |
| author_facet | Damar Riyadi Aina Musdholifah |
| author_sort | Damar Riyadi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T01:56:53Z |
| format | Article |
| id | doaj.art-d2189b3561f04b0eb624966c99dd15e9 |
| institution | Directory Open Access Journal |
| issn | 1978-1520 2460-7258 |
| language | English |
| last_indexed | 2024-12-22T01:56:53Z |
| publishDate | 2018-07-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| spelling | doaj.art-d2189b3561f04b0eb624966c99dd15e92022-12-21T18:42:45ZengUniversitas Gadjah MadaIJCCS (Indonesian Journal of Computing and Cybernetics Systems)1978-15202460-72582018-07-0112213914810.22146/ijccs.3042321674Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based ReasoningDamar Riyadi0Aina Musdholifah1Master of Computer Science, FMIPA UGM, YogyakartaDepartment of Electronics and Computer Science, FMIPA UGM, YogyakartaThis 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.https://jurnal.ugm.ac.id/ijccs/article/view/30423case-based reasoningindexing, clusteringLTKCnearest neighbor retrieval |
| spellingShingle | Damar Riyadi Aina Musdholifah Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based Reasoning IJCCS (Indonesian Journal of Computing and Cybernetics Systems) case-based reasoning indexing, clustering LTKC nearest neighbor retrieval |
| title | Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based Reasoning |
| title_full | Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based Reasoning |
| title_fullStr | Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based Reasoning |
| title_full_unstemmed | Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based Reasoning |
| title_short | Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based Reasoning |
| title_sort | local triangular kernel based clustering ltkc for case indexing on case based reasoning |
| topic | case-based reasoning indexing, clustering LTKC nearest neighbor retrieval |
| url | https://jurnal.ugm.ac.id/ijccs/article/view/30423 |
| work_keys_str_mv | AT damarriyadi localtriangularkernelbasedclusteringltkcforcaseindexingoncasebasedreasoning AT ainamusdholifah localtriangularkernelbasedclusteringltkcforcaseindexingoncasebasedreasoning |