Knowledge discovery for geographical cellular automata
Abstract This paper proposes a new method for geographical simulation by applying data mining techniques to cellular automata. CA has strong capabilities in simulating complex Systems. The core of CA is how to define transition rules. There are no good methods for defining these transition rules. Th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Xia [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2005 |
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| Schlagwörter: |
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| Anmerkung: |
© Science in China Press 2005 |
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| Übergeordnetes Werk: |
Enthalten in: Science in China / D - Science Press, 1996, 48(2005), 10 vom: 01. Okt., Seite 1758-1767 |
|---|---|
| Übergeordnetes Werk: |
volume:48 ; year:2005 ; number:10 ; day:01 ; month:10 ; pages:1758-1767 |
| Links: |
|---|
| DOI / URN: |
10.1360/01yd0368 |
|---|
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OLC2063778280 |
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10.1360/01yd0368 doi (DE-627)OLC2063778280 (DE-He213)01yd0368-p DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ 13 ssgn Li, Xia verfasserin aut Knowledge discovery for geographical cellular automata 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Science in China Press 2005 Abstract This paper proposes a new method for geographical simulation by applying data mining techniques to cellular automata. CA has strong capabilities in simulating complex Systems. The core of CA is how to define transition rules. There are no good methods for defining these transition rules. They are usually defined by using heuristic methods and thus subject to uncertainties. Mathematical equations are used to represent transition rules implicitly and have limitations in capturing complex relationships. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The proposed method can reduce the influences of individual knowledge and preferences in defining transition rules and generate more reliable simulation results. It can efficiently discover knowledge from a vast volume of spatial data. knowledge discovery cellular automata geographical simulation geographical information Systems Yeh, Anthony Gar-On aut Enthalten in Science in China / D Science Press, 1996 48(2005), 10 vom: 01. Okt., Seite 1758-1767 (DE-627)193118238 (DE-600)1307899-9 (DE-576)062316923 1006-9313 nnns volume:48 year:2005 number:10 day:01 month:10 pages:1758-1767 https://doi.org/10.1360/01yd0368 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-OAS SSG-OLC-MFO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_40 GBV_ILN_70 GBV_ILN_4082 AR 48 2005 10 01 10 1758-1767 |
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10.1360/01yd0368 doi (DE-627)OLC2063778280 (DE-He213)01yd0368-p DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ 13 ssgn Li, Xia verfasserin aut Knowledge discovery for geographical cellular automata 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Science in China Press 2005 Abstract This paper proposes a new method for geographical simulation by applying data mining techniques to cellular automata. CA has strong capabilities in simulating complex Systems. The core of CA is how to define transition rules. There are no good methods for defining these transition rules. They are usually defined by using heuristic methods and thus subject to uncertainties. Mathematical equations are used to represent transition rules implicitly and have limitations in capturing complex relationships. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The proposed method can reduce the influences of individual knowledge and preferences in defining transition rules and generate more reliable simulation results. It can efficiently discover knowledge from a vast volume of spatial data. knowledge discovery cellular automata geographical simulation geographical information Systems Yeh, Anthony Gar-On aut Enthalten in Science in China / D Science Press, 1996 48(2005), 10 vom: 01. Okt., Seite 1758-1767 (DE-627)193118238 (DE-600)1307899-9 (DE-576)062316923 1006-9313 nnns volume:48 year:2005 number:10 day:01 month:10 pages:1758-1767 https://doi.org/10.1360/01yd0368 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-OAS SSG-OLC-MFO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_40 GBV_ILN_70 GBV_ILN_4082 AR 48 2005 10 01 10 1758-1767 |
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10.1360/01yd0368 doi (DE-627)OLC2063778280 (DE-He213)01yd0368-p DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ 13 ssgn Li, Xia verfasserin aut Knowledge discovery for geographical cellular automata 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Science in China Press 2005 Abstract This paper proposes a new method for geographical simulation by applying data mining techniques to cellular automata. CA has strong capabilities in simulating complex Systems. The core of CA is how to define transition rules. There are no good methods for defining these transition rules. They are usually defined by using heuristic methods and thus subject to uncertainties. Mathematical equations are used to represent transition rules implicitly and have limitations in capturing complex relationships. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The proposed method can reduce the influences of individual knowledge and preferences in defining transition rules and generate more reliable simulation results. It can efficiently discover knowledge from a vast volume of spatial data. knowledge discovery cellular automata geographical simulation geographical information Systems Yeh, Anthony Gar-On aut Enthalten in Science in China / D Science Press, 1996 48(2005), 10 vom: 01. Okt., Seite 1758-1767 (DE-627)193118238 (DE-600)1307899-9 (DE-576)062316923 1006-9313 nnns volume:48 year:2005 number:10 day:01 month:10 pages:1758-1767 https://doi.org/10.1360/01yd0368 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-OAS SSG-OLC-MFO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_40 GBV_ILN_70 GBV_ILN_4082 AR 48 2005 10 01 10 1758-1767 |
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10.1360/01yd0368 doi (DE-627)OLC2063778280 (DE-He213)01yd0368-p DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ 13 ssgn Li, Xia verfasserin aut Knowledge discovery for geographical cellular automata 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Science in China Press 2005 Abstract This paper proposes a new method for geographical simulation by applying data mining techniques to cellular automata. CA has strong capabilities in simulating complex Systems. The core of CA is how to define transition rules. There are no good methods for defining these transition rules. They are usually defined by using heuristic methods and thus subject to uncertainties. Mathematical equations are used to represent transition rules implicitly and have limitations in capturing complex relationships. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The proposed method can reduce the influences of individual knowledge and preferences in defining transition rules and generate more reliable simulation results. It can efficiently discover knowledge from a vast volume of spatial data. knowledge discovery cellular automata geographical simulation geographical information Systems Yeh, Anthony Gar-On aut Enthalten in Science in China / D Science Press, 1996 48(2005), 10 vom: 01. Okt., Seite 1758-1767 (DE-627)193118238 (DE-600)1307899-9 (DE-576)062316923 1006-9313 nnns volume:48 year:2005 number:10 day:01 month:10 pages:1758-1767 https://doi.org/10.1360/01yd0368 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-OAS SSG-OLC-MFO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_40 GBV_ILN_70 GBV_ILN_4082 AR 48 2005 10 01 10 1758-1767 |
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10.1360/01yd0368 doi (DE-627)OLC2063778280 (DE-He213)01yd0368-p DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ 13 ssgn Li, Xia verfasserin aut Knowledge discovery for geographical cellular automata 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Science in China Press 2005 Abstract This paper proposes a new method for geographical simulation by applying data mining techniques to cellular automata. CA has strong capabilities in simulating complex Systems. The core of CA is how to define transition rules. There are no good methods for defining these transition rules. They are usually defined by using heuristic methods and thus subject to uncertainties. Mathematical equations are used to represent transition rules implicitly and have limitations in capturing complex relationships. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The proposed method can reduce the influences of individual knowledge and preferences in defining transition rules and generate more reliable simulation results. It can efficiently discover knowledge from a vast volume of spatial data. knowledge discovery cellular automata geographical simulation geographical information Systems Yeh, Anthony Gar-On aut Enthalten in Science in China / D Science Press, 1996 48(2005), 10 vom: 01. Okt., Seite 1758-1767 (DE-627)193118238 (DE-600)1307899-9 (DE-576)062316923 1006-9313 nnns volume:48 year:2005 number:10 day:01 month:10 pages:1758-1767 https://doi.org/10.1360/01yd0368 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-OAS SSG-OLC-MFO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_40 GBV_ILN_70 GBV_ILN_4082 AR 48 2005 10 01 10 1758-1767 |
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Abstract This paper proposes a new method for geographical simulation by applying data mining techniques to cellular automata. CA has strong capabilities in simulating complex Systems. The core of CA is how to define transition rules. There are no good methods for defining these transition rules. They are usually defined by using heuristic methods and thus subject to uncertainties. Mathematical equations are used to represent transition rules implicitly and have limitations in capturing complex relationships. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The proposed method can reduce the influences of individual knowledge and preferences in defining transition rules and generate more reliable simulation results. It can efficiently discover knowledge from a vast volume of spatial data. © Science in China Press 2005 |
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Abstract This paper proposes a new method for geographical simulation by applying data mining techniques to cellular automata. CA has strong capabilities in simulating complex Systems. The core of CA is how to define transition rules. There are no good methods for defining these transition rules. They are usually defined by using heuristic methods and thus subject to uncertainties. Mathematical equations are used to represent transition rules implicitly and have limitations in capturing complex relationships. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The proposed method can reduce the influences of individual knowledge and preferences in defining transition rules and generate more reliable simulation results. It can efficiently discover knowledge from a vast volume of spatial data. © Science in China Press 2005 |
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Abstract This paper proposes a new method for geographical simulation by applying data mining techniques to cellular automata. CA has strong capabilities in simulating complex Systems. The core of CA is how to define transition rules. There are no good methods for defining these transition rules. They are usually defined by using heuristic methods and thus subject to uncertainties. Mathematical equations are used to represent transition rules implicitly and have limitations in capturing complex relationships. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The proposed method can reduce the influences of individual knowledge and preferences in defining transition rules and generate more reliable simulation results. It can efficiently discover knowledge from a vast volume of spatial data. © Science in China Press 2005 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2063778280</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504094344.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2005 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1360/01yd0368</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2063778280</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)01yd0368-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">13</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Xia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Knowledge discovery for geographical cellular automata</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2005</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Science in China Press 2005</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper proposes a new method for geographical simulation by applying data mining techniques to cellular automata. CA has strong capabilities in simulating complex Systems. The core of CA is how to define transition rules. There are no good methods for defining these transition rules. They are usually defined by using heuristic methods and thus subject to uncertainties. Mathematical equations are used to represent transition rules implicitly and have limitations in capturing complex relationships. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The proposed method can reduce the influences of individual knowledge and preferences in defining transition rules and generate more reliable simulation results. It can efficiently discover knowledge from a vast volume of spatial data.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">knowledge discovery</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">cellular automata</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">geographical simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">geographical information Systems</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yeh, Anthony Gar-On</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Science in China / D</subfield><subfield code="d">Science Press, 1996</subfield><subfield code="g">48(2005), 10 vom: 01. Okt., Seite 1758-1767</subfield><subfield code="w">(DE-627)193118238</subfield><subfield code="w">(DE-600)1307899-9</subfield><subfield code="w">(DE-576)062316923</subfield><subfield code="x">1006-9313</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:48</subfield><subfield code="g">year:2005</subfield><subfield code="g">number:10</subfield><subfield code="g">day:01</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:1758-1767</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1360/01yd0368</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-OAS</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MFO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4082</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">48</subfield><subfield code="j">2005</subfield><subfield code="e">10</subfield><subfield code="b">01</subfield><subfield code="c">10</subfield><subfield code="h">1758-1767</subfield></datafield></record></collection>
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