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Enhancing Spatial Association Rule Mining

Learn about spatial association rules, support and confidence metrics, hierarchy structuring, ILP algorithms, and challenges in mining strong spatial rules. Expllore examples and algorithms to improve data analysis.

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Enhancing Spatial Association Rule Mining

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  1. Spatial Association

  2. Spatial Association Rule • X → Y • P1 ..  Pm → Q1  ..  Qn (c%) • Los predicaat: 1-predicate • Set van P1 t/m Pk: k-predicate

  3. Support & confidence • sup(X → Y) • de kans dat de implicatie voorkomt in de dataset • conf(X → Y) = sup(X → Y) / sup(X)

  4. Sterkte van een regel • Sterk = relaties in een regel komen vaker voor • Sterk = hoge support en confidence • … maar hoe hoog?

  5. Minimum support/confidence • Input voor mining algoritme • Voor elk niveau in de hiërarchie • minsup[l] • minconf[l]

  6. Voorbeelden (1) • Spatial antecedenten, non-spatial consequent • is_a(x, house)  close_to(x, beach) → is_expensive(x) (85%)

  7. Voorbeelden (2) • (Non)-spatial antecedenten, spatial consequent • is_a(x, gas_station) → close_to(x, highway) (50%)

  8. Voorbeelden (3) • Combineren van support & confidence • is_a(A, large_town)  intersects(A,B)  adjacent_to(A, C) → is_a(B,motorway), C!=B, is_a(C,sea) (36%,80%

  9. Hiërarchieën (1) • Data structureren in spatial hierarchy • Efficiënter • Processing gaat ‘top-down’ • Progressief zoeken • (road (national_highway (A27, A2, …), provincial_highway (N238, N235, …), city_street (Oudegracht, Heidelberglaan, …), …), …)

  10. Hiërarchieën (2) water river sea lake Rijn Large lake Waddenzee Noordzee IJsselmeer

  11. ILP Algoritme (1) • Gebaseerd op ILP • Inductive Logic Programming • ‘feature extraction’ om een deductive database te krijgen • Logische variant van relational database

  12. ILP Algoritme (2) • Spatial observations • O[Amsterdam | Amsterdam]is_a(Amsterdam, large_town)intersects(Amsterdam, A2)intersects(Amsterdam, A10)close_to(Amsterdam, Schiphol)…

  13. ILP Algoritme (3) • O[Schiphol | Amsterdam]is_a(Schiphol, airport)intersects(Schiphol, A4)… • O[s] = O[s|s]  O[ri|s’]

  14. ILP Algoritme (4) • Twee subproblemen • Find large/frequent spatial patterns • Generate strong spatial association rules • Prunen

  15. Algoritme #2 (1) • Vindt sterke multi-level spatial association rules • Beperking zoekruimte • Relevant object set • Concept hierarchies • minsup/minconf

  16. Algoritme #2 (2) • Predicaten berekenen op hoog niveau (lage resolutie) • Prunen adhv minsup[1] • Overblijvende predicaten berekenen op lagere niveaus

  17. Algoritme #2 (3) • Alle k-predicates (voor alle k’s) berekenen • Prunen adhv minconf[l]

  18. Conclusie • Veel problemen • Schaalbaarheid • Ontbrekende data • Nog veel user input nodig • O.a. minsup/minconf • Aantal oplossingen • Maar niet eenduidig

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