A comparative study on ILP-based concept discovery systems
►For a concept discovery task, the user should consider several points for selecting the most suitable system. ► If the target concept is not explicit, using WARMR is a wise choice in order to see various frequent clauses hidden in the data. ► If the user is familiar with mode declarations, search a...
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| Published in: | Expert systems with applications Vol. 38; no. 9; pp. 11598 - 11607 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.09.2011
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | ►For a concept discovery task, the user should consider several points for selecting the most suitable system. ► If the target concept is not explicit, using WARMR is a wise choice in order to see various frequent clauses hidden in the data. ► If the user is familiar with mode declarations, search and evaluation mechanisms, PROGOL and ALEPH are suitable choices. ► For a naive user, who has the data available in the data-base and who does not have information on inner working of concept discovery mechanisms, C
2D provides a simple, yet effective mechanism.
Inductive Logic Programming (ILP) studies learning from examples, within the framework provided by clausal logic. ILP has become a popular subject in the field of data mining due to its ability to discover patterns in relational domains. Several ILP-based concept discovery systems are developed which employs various search strategies, heuristics and language pattern limitations. LINUS, GOLEM, CIGOL, MIS, FOIL, PROGOL, ALEPH and WARMR are well-known ILP-based systems. In this work, firstly introductory information about ILP is given, and then the above-mentioned systems and an ILP-based concept discovery system called C
2D are briefly described and the fundamentals of their mechanisms are demonstrated on a running example. Finally, a set of experimental results on real-world problems are presented in order to evaluate and compare the performance of the above-mentioned systems. |
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| AbstractList | ►For a concept discovery task, the user should consider several points for selecting the most suitable system. ► If the target concept is not explicit, using WARMR is a wise choice in order to see various frequent clauses hidden in the data. ► If the user is familiar with mode declarations, search and evaluation mechanisms, PROGOL and ALEPH are suitable choices. ► For a naive user, who has the data available in the data-base and who does not have information on inner working of concept discovery mechanisms, C
2D provides a simple, yet effective mechanism.
Inductive Logic Programming (ILP) studies learning from examples, within the framework provided by clausal logic. ILP has become a popular subject in the field of data mining due to its ability to discover patterns in relational domains. Several ILP-based concept discovery systems are developed which employs various search strategies, heuristics and language pattern limitations. LINUS, GOLEM, CIGOL, MIS, FOIL, PROGOL, ALEPH and WARMR are well-known ILP-based systems. In this work, firstly introductory information about ILP is given, and then the above-mentioned systems and an ILP-based concept discovery system called C
2D are briefly described and the fundamentals of their mechanisms are demonstrated on a running example. Finally, a set of experimental results on real-world problems are presented in order to evaluate and compare the performance of the above-mentioned systems. Inductive Logic Programming (ILP) studies learning from examples, within the framework provided by clausal logic. ILP has become a popular subject in the field of data mining due to its ability to discover patterns in relational domains. Several ILP-based concept discovery systems are developed which employs various search strategies, heuristics and language pattern limitations. LINUS, GOLEM, CIGOL, MIS, FOIL, PROGOL, ALEPH and WARMR are well-known ILP-based systems. In this work, firstly introductory information about ILP is given, and then the above-mentioned systems and an ILP-based concept discovery system called C[super]2D are briefly described and the fundamentals of their mechanisms are demonstrated on a running example. Finally, a set of experimental results on real-world problems are presented in order to evaluate and compare the performance of the above-mentioned systems. |
| Author | Senkul, P. Toroslu, I.H. Kavurucu, Y. |
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| References_xml | – reference: (Vol. 63, pp. 33–44.). SIGART Newsletter, ACM, Hawaii. – reference: Yin, X., Han, J., Yang, J., Yu, P. S. (2004). Crossmine: Efficient classification across multiple database relations. In – volume: 70 start-page: 121 year: 2008 end-page: 133 ident: b0105 article-title: QG/GA: A stochastic search for progol publication-title: Machine Learning – year: 1983 ident: b0115 article-title: Algorithmic Program Debugging – reference: (Vol. 1297, pp. 273–287). Springer-Verlag ( – reference: (pp. 339–351). Morgan Kaufmann. – reference: (pp. 399–411). – reference: (pp. 446–451). Hong Kong. – volume: 5 start-page: 1 year: 2003 end-page: 16 ident: b0030 article-title: Multi-relational data mining: An introduction publication-title: SIGKDD Explorations – reference: Srinivasan, A., King, R. D., Muggleton, S., Sternberg, M. J. E. (1997). Carcinogenesis predictions using ILP. In – reference: Srinivasan, A. (1999). The aleph manual. – reference: (pp. 43–50). Netherland: Amsterdam. – volume: 36 start-page: 11418 year: 2009 end-page: 11428 ident: b0060 article-title: ILP-based concept discovery in multi-relational data mining publication-title: Expert Systems with Applications – year: 2001 ident: b0035 article-title: Relational Data Mining – reference: (pp. 1–14). – reference: Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A. I. (1996). Fast discovery of association rules. In – year: 1996 ident: b0125 publication-title: The role of background knowledge: Using a problem from chemistry to examine the performance of an ILP program – reference: Kavurucu, Y., Senkul, P., Toroslu, I. H. (2009a). Analyzing transitive rules on a hybrid concept discovery system. In: – reference: Lavrač, N., Džeroski, S., Grobelnik, M. (1991). Learning nonrecursive definitions of relations with LINUS. In Y. Kodratoff (Ed.), – volume: 5 start-page: 239 year: 1990 end-page: 266 ident: b0110 article-title: Learning logical definitions from relations publication-title: Machine Learning – reference: . Ph.D. thesis, Utrecht, Netherlands: Utrecht University. – reference: . MIT Press. – reference: Knobbe, A. J. (2004). – reference: Muggleton, S., 1999. Inductive Logic Programming. In – start-page: 345 year: 2002 end-page: 359 ident: b0140 publication-title: Overview of inductive logic programming (ILP) systems – reference: . Tech. rep., PRG-TR-8-95 Oxford University Computing Laboratory. – reference: Srinivasan, A., Muggleton, S., King, R., Sternberg, M. (1995). – reference: (pp. 368–381). Tokyo, Japan: Ohmsma, – reference: Muggleton, S., Buntine, W. (1988). Machine invention of first order predicates by inverting resolution. In – reference: (Vol. 5572/2009, pp. 227–234). Springer Berlin/Heidelberg. – reference: Kavurucu, Y., Senkul, P., Toroslu, I. H. (2008a). Aggregation in confidence-based concept discovery for multi-relational data mining. In – reference: Dehaspe, L., Raedt, L. D. (1997). Mining association rules in multiple relations. In ILP’97: – year: 1994 ident: b0070 article-title: Inductive logic programming: Techniques and applications – reference: Muggleton, S. (1995). – volume: 5 start-page: 80 year: 2003 end-page: 81 ident: b0025 article-title: Prospects and challenges for multi-relational data mining publication-title: SIGKDD Explorations – reference: (pp. 307–328) AAAI/MIT Press. – reference: Muggleton, S., Feng, C. (1990). Efficient induction of logic programs. In – reference: Michalski, R., Larson, J. (1997). Inductive inference of VL decision rules. In – reference: (pp. 125–132.) London, UK: Springer-Verlag. – reference: Kavurucu, Y., Senkul, P., Toroslu, I. H., March 2008b. Confidence-based concept discovery in multi-relational data mining. In – reference: . New Generation Computing, Special issue on Inductive Logic Programming 13(3–4) (pp. 245–286). – start-page: 189 year: 2001 end-page: 212 ident: b0015 article-title: Discovery of relational association rules publication-title: Relational data mining – year: 1992 ident: b0020 article-title: The application of inductive logic programming to finite element mesh design publication-title: Inductive logic programming – reference: (Vol. 482, pp. 265–281.) Lecture Notes in Artificial Intelligence. Springer-Verlag. – reference: . – reference: ). – reference: Dzeroski, S. (2006). From inductive logic programming to relational data mining. In – ident: 10.1016/j.eswa.2011.03.038_b0135 – ident: 10.1016/j.eswa.2011.03.038_b0075 doi: 10.1007/BFb0017020 – ident: 10.1016/j.eswa.2011.03.038_b0005 – volume: 5 start-page: 1 issue: 1 year: 2003 ident: 10.1016/j.eswa.2011.03.038_b0030 article-title: Multi-relational data mining: An introduction publication-title: SIGKDD Explorations doi: 10.1145/959242.959245 – year: 1983 ident: 10.1016/j.eswa.2011.03.038_b0115 – year: 1992 ident: 10.1016/j.eswa.2011.03.038_b0020 article-title: The application of inductive logic programming to finite element mesh design – year: 1996 ident: 10.1016/j.eswa.2011.03.038_b0125 – volume: 5 start-page: 80 year: 2003 ident: 10.1016/j.eswa.2011.03.038_b0025 article-title: Prospects and challenges for multi-relational data mining publication-title: SIGKDD Explorations doi: 10.1145/959242.959252 – ident: 10.1016/j.eswa.2011.03.038_b0040 doi: 10.1007/11853886_1 – ident: 10.1016/j.eswa.2011.03.038_b0120 – ident: 10.1016/j.eswa.2011.03.038_b0145 – year: 1994 ident: 10.1016/j.eswa.2011.03.038_b0070 – ident: 10.1016/j.eswa.2011.03.038_b0080 – start-page: 345 year: 2002 ident: 10.1016/j.eswa.2011.03.038_b0140 publication-title: Overview of inductive logic programming (ILP) systems – ident: 10.1016/j.eswa.2011.03.038_b0130 doi: 10.1007/3540635149_56 – ident: 10.1016/j.eswa.2011.03.038_b0065 – ident: 10.1016/j.eswa.2011.03.038_b0050 – start-page: 189 year: 2001 ident: 10.1016/j.eswa.2011.03.038_b0015 article-title: Discovery of relational association rules – ident: 10.1016/j.eswa.2011.03.038_b0010 – ident: 10.1016/j.eswa.2011.03.038_b0095 doi: 10.1016/B978-0-934613-64-4.50040-2 – ident: 10.1016/j.eswa.2011.03.038_b0085 doi: 10.1007/BF03037227 – ident: 10.1016/j.eswa.2011.03.038_b0090 – volume: 5 start-page: 239 issue: 3 year: 1990 ident: 10.1016/j.eswa.2011.03.038_b0110 article-title: Learning logical definitions from relations publication-title: Machine Learning doi: 10.1007/BF00117105 – ident: 10.1016/j.eswa.2011.03.038_b0100 – year: 2001 ident: 10.1016/j.eswa.2011.03.038_b0035 – volume: 36 start-page: 11418 issue: 9 year: 2009 ident: 10.1016/j.eswa.2011.03.038_b0060 article-title: ILP-based concept discovery in multi-relational data mining publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2009.02.100 – ident: 10.1016/j.eswa.2011.03.038_b0045 – volume: 70 start-page: 121 issue: 2–3 year: 2008 ident: 10.1016/j.eswa.2011.03.038_b0105 article-title: QG/GA: A stochastic search for progol publication-title: Machine Learning doi: 10.1007/s10994-007-5029-3 – ident: 10.1016/j.eswa.2011.03.038_b0055 doi: 10.1007/978-3-642-02319-4_27 |
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| Title | A comparative study on ILP-based concept discovery systems |
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