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‫‪11th International CSI Computer Conference (CSICC’2006), School of Computer Science, IPM, Jan. 24-26, 2006, Tehran, Iran.‬‬
‫ﮐﺸﻒ ﻗﻮﺍﻋﺪ ﺣﺎﮐﻢ ﺑﻴﻦ ﺩﺍﺩﻩﻫﺎ ﺩﺭ ﭘﺎﻳﮕﺎﻩﺩﺍﺩﻩ ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺭﻭﻳﮑﺮﺩ ﻣﻨﻄﻖ‬
‫ﺍﺳﺘﻘﺮﺍﺋﻲ ‪ ‬‬
‫‪ ‬‬
‫‪ ‬‬
‫ﭼﮑﻴﺪﻩ‬
‫‪ ‬ﺑﺮﻧﺎﻣﻪﻧﻮﻳﺴﻲ ﻣﻨﻄﻘﻲ ﺍﺳﺘﻘﺮﺍﺋﻲ ﺑﻪ ﻋﻨﻮﺍﻥ ﻧﻘﻄﻪ ﻣﺸﺘﺮﮎ ﺑﺮﻧﺎﻣﻪﻧﻮﻳﺴﻲ ﻣﻨﻄﻘﻲ ﻭ ﻳﺎﺩﮔﻴﺮﻱ ﻣﺎﺷﻴﻦ ﺍﺳﺘﻘﺮﺍﺋﻲ ﺩﺭ ﺳﺎﻝﻫﺎﻱ ﺍﺧﻴﺮ ﺭﺷﺪ ﻭ‬
‫ﺗﻮﺳﻌﻪ ﭼﺸﻤﮕﻴﺮﻱ ﭘﻴﺪﺍ ﮐﺮﺩﻩ ﺍﺳﺖ ﻭ ﺩﺭ ﺣﻞ ﻣﺴﺎﺋﻞ ﻣﺨﺘﻠﻔﻲ ﺑﻪ ﮐﺎﺭ ﮔﺮﻓﺘﻪ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺍﺯ ﻃﺮﻑ ﺩﻳﮕﺮ ﭘﻴﺪﺍ ﮐﺮﺩﻥ ﺭﻭﺵﻫﺎﻱ ﮐﺎﺭﺁﻣﺪ‬
‫ﻭ ﻣﻨﺎﺳﺐ ﺑﺮﺍﻱ ﺣﻞ ﻣﺴﺎﺋﻠﻲ ﺩﺭ ﺣﻮﺯﻩﻫﺎﻳﻲ ﻧﻈﻴﺮ ﺍﺳﺘﺨﺮﺍﺝ ﺩﺍﻧﺶ ﺍﺯ ﺑﺎﻧﮏﻫﺎﻱ ﺍﻃﻼﻋﺎﺗﻲ‪ ،‬ﺩﺍﺩﻩﮐﺎﻭﻱ ﻭ ﻭﺏﮐﺎﻭﻱ ﻳﮑﻲ ﺍﺯ ﻧﻴﺎﺯﻫﺎﻱ‬
‫ﺍﺳﺎﺳﻲ ﺗﺤﻘﻴﻘﺎﺗﻲ ﺩﺭ ﺳﺎﻟﻬﺎﻱ ﺍﺧﻴﺮ ﺑﻪ ﺷﻤﺎﺭ ﻣﻲﺁﻳﺪ‪ .‬ﺩﺭ ﺍﻳﻦ ﻣﻘﺎﻟﻪ ﺳﻌﻲ ﺷﺪﻩ ﺍﺳﺖ ﺑﻪ ﮐﻤﮏ ‪ ILP١‬ﺭﻭﺷﻲ ﺑﺮﺍﻱ ﮐﺸﻒ ﻗﻮﺍﻋﺪ ﺣﺎﮐﻢ‬
‫ﺩﺭ ﺑﺎﻧﮏﻫﺎﻱ ﺍﻃﻼﻋﺎﺗﻲ ﺍﺭﺍﺋﻪ ﺷﻮﺩ‪ .‬ﺩﺭ ﺍﻳﻦ ﺭﻭﺵ ﺍﻃﻼﻋﺎﺕ ﻣﻮﺟﻮﺩ ﺩﺭ ﺑﺎﻧﮏ ﺍﻃﻼﻋﺎﺗﻲ ﺑﻪ ﻣﺜﺎﻝﻫﺎﻱ ﻣﺜﺒﺖ ﻭ ﻣﻨﻔﻲ ﮐﻪ ﻭﺭﻭﺩﻱﻫﺎﻱ‬
‫ﺍﺻﻠﻲ ‪ ILP‬ﺭﺍ ﺗﺸﮑﻴﻞ ﻣﻲﺩﻫﻨﺪ‪ ،‬ﺗﺒﺪﻳﻞ ﻣﻲﺷﻮﺩ ﻭ ﺑﻪ ﮐﻤﮏ ﺍﺑﺰﺍﺭﻫﺎﻱ ‪ ILP‬ﺭﺍﺑﻄﻪ ﺑﻴﻦ ﺍﻃﻼﻋﺎﺕ ﺑﻪ ﺻﻮﺭﺕ ﻗﻮﺍﻋﺪ ﻣﻨﻄﻘﻲ ﺑﻪ ﺩﺳﺖ‬
‫ﻣﻲﺁﻳﺪ‪ .‬ﺍﻳﻦ ﺭﻭﺵ ﺭﻭﻱ ﻳﮏ ﭘﺎﻳﮕﺎﻩﺩﺍﺩﻩ ﻧﻤﻮﻧﻪ ﺁﺯﻣﺎﻳﺶ ﺷﺪﻩ ﺍﺳﺖ‪.‬‬
‫ﮐﻠﻤﺎﺕ ﮐﻠﻴﺪﻱ‪ :‬ﺑﺮﻧﺎﻣﻪﻧﻮﻳﺴﻲ ﻣﻨﻄﻘﻲ ﺍﺳﺘﻘﺮﺍﺋﻲ‪ ،‬ﺍﺳﺘﺨﺮﺍﺝ ﺩﺍﻧﺶ ﺍﺯ ﭘﺎﻳﮕﺎﻩﺩﺍﺩﻩ‪ ،‬ﺩﺍﺩﻩﮐﺎﻭﻱ ‪ ‬‬
‫‪ -۱‬ﻣﻘﺪﻣﻪ ‪ ‬‬
‫‪ ‬ﺭﻭﺵ ﺑﺮﻧﺎﻣﻪﻧﻮﻳﺴﻲ ﻣﻨﻄﻘﻲ ﺍﺳﺘﻘﺮﺍﻳﻲ ﺍﺯ ﺍﻭﺍﺋﻞ ﺩﻫﻪ ‪ ۷۰‬ﻣﻴﻼﺩﻱ ﻣﻄﺮﺡ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺳﺎﻟﻬﺎﻱ ‪ ۱۹۸۷‬ﺗﺎ ‪ ۱۹۹۶‬ﻋﺼﺮ ﻃﻼﻳﻲ ﺍﻳﻦ ﺭﻭﻳﮑﺮﺩ‬
‫ﺑﻪ ﺷﻤﺎﺭ ﻣﻲﺁﻳﺪ‪ .‬ﺩﺭ ﻃﻲ ﺍﻳﻦ ﺳﺎﻝﻫﺎ ‪ ILP‬ﺩﺭ ﺣﻞ ﻣﺴﺎﺋﻞ ﻣﺨﺘﻠﻔﻲ ﺑﻪ ﮐﺎﺭ ﮔﺮﻓﺘﻪ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺍﺯ ﻃﺮﻑ ﺩﻳﮕﺮ ﺩﺭ ﺳﺎﻝﻫﺎﻱ ﺍﺧﻴﺮ‬
‫ﺑﺤﺚﻫﺎﻳﻲ ﻧﻈﻴﺮ ﺍﺳﺘﺨﺮﺍﺝ ﺩﺍﻧﺶ ﺍﺯ ﭘﺎﻳﮕﺎﻩ ﺩﺍﺩﻩﻫﺎ‪ ،‬ﺩﺍﺩﻩﮐﺎﻭﻱ ﻭ ﻭﺏﮐﺎﻭﻱ ﺑﺴﻴﺎﺭ ﻣﻮﺭﺩ ﺗﻮﺟﻪ ﻗﺮﺍﺭ ﮔﺮﻓﺘﻪ ﺍﺳﺖ‪ .‬ﻣﺎ ﺩﺭ ﺍﻳﻦ ﻣﻘﺎﻟﻪ ﺳﻌﻲ‬
‫ﺧﻮﺍﻫﻴﻢ ﮐﺮﺩ ﺗﺎ ﺭﻭﺷﻲ ﺟﺪﻳﺪ ﺑﺮﺍﻱ ﺍﺳﺘﺨﺮﺍﺝ ﺩﺍﻧﺶ ﻭ ﻗﻮﺍﻋﺪ ﺣﺎﮐﻢ ﺑﻴﻦ ﻣﻮﺟﻮﺩﻳﺖﻫﺎﻱ ﺍﻃﻼﻋﺎﺗﻲ ﺩﺭ ﭘﺎﻳﮕﺎﻩ ﺩﺍﺩﻩﻫﺎ ﺭﺍ ﺍﺭﺍﺋﻪ ﺩﻫﻴﻢ‪.‬‬
‫‪ ‬ﺩﺭ ﺍﺩﺍﻣﻪ ﺍﻳﻦ ﻣﻘﺎﻟﻪ ﺩﺭ ﺑﺨﺶ ‪ ۲‬ﺑﻪ ﻣﻌﺮﻓﻲ ﺍﺟﻤﺎﻟﻲ ‪ ILP‬ﺧﻮﺍﻫﻴﻢ ﭘﺮﺩﺍﺧﺖ‪ .‬ﺑﺨﺶ ‪ ۳‬ﺑﻪ ﻣﺮﻭﺭ ﮐﺎﺭﻫﺎﻱ ﻣﺸﺎﺑﻪ ﺍﺧﺘﺼﺎﺹ ﺩﺍﺭﺩ‪ .‬ﺩﺭ‬
‫ﺑﺨﺶ ‪ ۴‬ﺭﻭﺵ ﭘﻴﺸﻨﻬﺎﺩﻱ ﺑﺮﺍﻱ ﮐﺸﻒ ﺭﻭﺍﺑﻂ ﺩﺭ ﺑﺎﻧﮏﻫﺎﻱ ﺍﻃﻼﻋﺎﺗﻲ ﺑﻪ ﮐﻤﮏ ‪ ILP‬ﺗﻮﺿﻴﺢ ﺩﺍﺩﻩ ﻣﻲﺷﻮﺩ‪ .‬ﺩﺭ ﻧﻬﺎﻳﺖ ﺑﺨﺶ ‪ ۵‬ﺑﻪ‬
‫ﻧﺘﻴﺠﻪﮔﻴﺮﻱ ﻭ ﺍﺭﺯﻳﺎﺑﻲ ﺭﻭﻳﮑﺮﺩ ﺍﻧﺘﺨﺎﺑﻲ ﺍﺧﺘﺼﺎﺹ ﺩﺍﺭﺩ‪.‬‬
‫‪ -۲‬ﺑﺮﻧﺎﻣﻪﻧﻮﻳﺴﻲ ﻣﻨﻄﻘﻲ ﺍﺳﺘﻘﺮﺍﺋﻲ ‪ ‬‬
‫‪ ILP‬ﺭﺍ ﻣﻲﺗﻮﺍﻥ ﺑﻪ ﻋﻨﻮﺍﻥ ﻓﺼﻞ ﻣﺸﺘﺮﮎ ﺑﺮﻧﺎﻣﻪﻧﻮﻳﺴﻲ ﻣﻨﻄﻘﻲ ﻭ ﻳﺎﺩﮔﻴﺮﻱ ﻣﺎﺷﻴﻦ ﺍﺳﺘﻘﺮﺍﺋﻲ ﺩﺍﻧﺴﺖ‪ .‬ﺩﺭ ﺭﻭﻳﮑﺮﺩ ‪ ILP‬ﺩﺭ ﭘﻲ ﺍﻳﻦ‬
‫ﻫﺴﺘﻴﻢ ﮐﻪ ﺑﺮ ﺍﺳﺎﺱ ﺷﻮﺍﻫﺪ ﻭ ﺍﻃﻼﻋﺎﺕ ﻣﻮﺟﻮﺩ ﻭ ﺑﺮ ﻣﺒﻨﺎﻱ ﺍﺳﺘﻘﺮﺍﺀ‪ ،‬ﻗﻮﺍﻋﺪ ﮐﻠﻲ ﺣﺎﮐﻢ ﺑﺮ ﺁﻥ ﺍﻃﻼﻋﺎﺕ ﺭﺍ ﺑﻪ ﺩﺳﺖ ﺁﻭﺭﻳﻢ‪ .‬ﺩﺭ‬
‫ﺭﻭﻳﮑﺮﺩ ‪ ،ILP‬ﻣﺴﺎﺋﻞ ﺑﻪ ﺻﻮﺭﺕ ﺯﻳﺮ ﺍﺭﺍﺋﻪ ﻣﻲﺷﻮﻧﺪ]‪:[Dze96‬‬
‫ﻣﺜﺎﻝ‪ : ‬ﻣﺠﻤﻮﻋﻪﺍﻱ ﺍﺯ ﻣﺜﺎﻝﻫﺎﻱ ﻳﺎﺩﮔﻴﺮﻱ ﻣﺜﺒﺖ )‪ (E+‬ﻭ ﻣﻨﻔﻲ )‪ (E-‬ﺑﺮﺍﻱ ﮔﺰﺍﺭﻩ ‪.P‬‬
‫ﺯﺑﺎﻥ ﺗﻮﺻﻴﻒ ﻣﻔﺎﻫﻴﻢ ‪ : L‬ﺯﺑﺎﻥ ﻣﻨﻄﻖ ﮔﺰﺍﺭﻩﻫﺎﻱ ﻣﺮﺗﺒﻪ ﺍﻭﻝ‬
‫‪٢‬‬
‫ﺩﺍﻧﺶ ﭘﻴﺶ ﺯﻣﻴﻨﻪ ‪ : B‬ﮔﺰﺍﺭﻩﻫﺎﻱ ‪ Pi‬ﺭﺍ ﺗﻌﺮﻳﻒ ﻣﻲﮐﻨﺪ ﮐﻪ ﻣﻲﺗﻮﺍﻧﻨﺪ ﺩﺭ ﺗﻌﺮﻳﻒ ‪ P‬ﺑﻪ ﮐﺎﺭ ﮔﺮﻓﺘﻪ ﺷﻮﻧﺪ‪.‬‬
‫ﻫﺪﻑ‪ : ‬ﭘﻴﺪﺍ ﮐﺮﺩﻥ ﻳﮏ ﻓﺮﺿﻴﻪ ‪ H‬ﺑﺮﺍﻱ ‪ P‬ﮐﻪ ﺩﺭ ‪ L‬ﺑﻴﺎﻥ ﺷﺪﻩ ﺑﺎﺷﺪ ﻭ ‪ H‬ﮐﺎﻣﻞ ﻭ ﺳﺎﺯﮔﺎﺭ ﺑﺎﺷﺪ‪.‬‬
‫‪ ‬‬
‫‪1‬‬
‫‪ Inductive Logic Programming ‬‬
‫‪2‬‬
‫‪ First-Order Predicate Logic ‬‬
‫‪ H‬ﮐﺎﻣﻞ ﺍﺳﺖ ﺍﮔﺮ ‪ ∀e ∈ E + : B ∧ H → e‬ﺑﺮﻗﺮﺍﺭ ﺑﺎﺷﺪ‪.‬‬
‫‪ H‬ﺳﺎﺯﮔﺎﺭ ﺍﺳﺖ ﺍﮔﺮ ‪ ∀e ∈ E − : B ∧ H a e‬ﺑﺮﻗﺮﺍﺭ ﺑﺎﺷﺪ‪.‬‬
‫‪ ‬‬
‫‪ -۳‬ﺳﺎﺑﻘﻪ ﺗﺤﻘﻴﻘﺎﺕ‬
‫ﻣﻨﻄﻖ ﻣﺮﺗﺒﻪ ﺍﻭﻝ ﺍﺑﺰﺍﺭ ﻣﻨﺎﺳﺒﻲ ﺑﺮﺍﻱ ﺑﻴﺎﻥ ﺩﺍﻧﺶ ﺗﻮﺻﻴﻔﻲ‪ ١‬ﺑﻪ ﺷﻤﺎﺭ ﻣﻲ ﺁﻳﺪ‪ ILP .‬ﺭﻭﺷﻲ ﺭﺍ ﻓﺮﺍﻫﻢ ﻣﻲﺁﻭﺭﺩ ﮐﻪ ﺑﻪ ﮐﻤﮏ ﺁﻥ‬
‫ﻣﻲﺗﻮﺍﻧﻴﻢ ﻓﺮﻣﻮﻝﻫﺎ ﻭ ﻗﻮﺍﻋﺪ ﻣﻨﻄﻘﻲ ﺭﺍ ﺍﺯ ﺭﻭﻱ ﺩﺍﺩﻩﻫﺎ ﺍﺳﺘﺨﺮﺍﺝ ﮐﻨﻴﻢ]‪ .[Mug95],[Mit97‬ﻣﻨﺒﻊ ﺍﺻﻠﻲ ﺍﺳﺘﺨﺮﺍﺝ ﺍﻳﻦ ﺍﻃﻼﻋﺎﺕ‬
‫ﻓﺎﻳﻞﻫﺎﻱ ‪ log‬ﺍﺳﺖ‪ .‬ﺍﻭ ﺳﻌﻲ ﮐﺮﺩﻩ ﺍﺳﺖ ﺗﺎ ﺭﻭﺷﻲ ﺑﺮﺍﻱ ﺗﻮﻟﻴﺪ ﻗﻮﺍﻋﺪ ﻣﻨﻄﻖ ﻣﺮﺗﺒﻪ ﺍﻭﻝ ﺑﺮﺍﻱ ﺍﻟﮕﻮﻫﺎﻱ ﺗﺮﺍﻓﻴﮑﻲ ﻭﺏ ﭘﻴﺪﺍ ﮐﻨﺪ‪.‬‬
‫‪ Loggie‬ﺍﺯ ‪ ILP‬ﺩﺭ ﺑﺎﺯﻳﺎﺑﻲ ﺍﻃﻼﻋﺎﺕ ﺍﺯ ﮐﺘﺎﺑﺨﺎﻧﻪﻫﺎﻱ ﺍﻟﮑﺘﺮﻭﻧﻴﮑﻲ ﺍﺳﺘﻔﺎﺩﻩ ﮐﺮﺩﻩ ﺍﺳﺖ]‪ DARPA .[Log00‬ﭘﺲ ﺍﺯ ﺣﺎﺩﺛﻪ ‪۱۱‬‬
‫ﺳﭙﺘﺎﻣﺒﺮ ‪ ۲۰۰۱‬ﭘﺮﻭﮊﻩﺍﻱ ﺭﺍ ﺑﺮﺍﻱ ﺷﻨﺎﺳﺎﻳﻲ ﻭ ﻣﺒﺎﺭﺯﻩ ﺑﺎ ﻓﻌﺎﻟﻴﺖﻫﺎﻱ ﺗﺮﻭﺭﻳﺴﺘﻲ ﺁﻏﺎﺯ ﮐﺮﺩ‪ .‬ﺩﺭ ﺍﻳﻦ ﭘﺮﻭﮊﻩ ﺍﺯ ‪ ILP‬ﺑﻪ ﻣﻨﻈﻮﺭ ﺷﻨﺎﺳﺎﻳﻲ‬
‫ﺍﻟﮕﻮﻫﺎﻱ ﭘﻴﭽﻴﺪﻩ ﺭﺍﺑﻄﻪﺍﻱ ﺩﺭ ﭘﺎﻳﮕﺎﻩ ﺩﺍﺩﻩﻫﺎﻱ ﺑﺰﺭﮒ ﺍﺳﺘﻔﺎﺩﻩ ﺷﺪﻩ ﺍﺳﺖ]‪.[Moo02‬‬
‫‪ -۴‬ﺭﻭﺵ ﭘﻴﺸﻨﻬﺎﺩﻱ ﺩﺭ ﺍﺳﺘﺨﺮﺍﺝ ﺩﺍﻧﺶ ﺑﻪ ﮐﻤﮏ ‪ILP‬‬
‫ﻣﺮﺍﺣﻞ ﮐﺸﻒ ﻗﻮﺍﻋﺪ ﺑﻴﻦ ﺩﺍﺩﻩﻫﺎ ﺩﺭ ﺍﺩﺍﻣﻪ ﻃﻲ ﭼﻬﺎﺭ ﻣﺮﺣﻠﻪ ﺍﺭﺍﺋﻪ ﻣﻲﺷﻮﺩ‪ .‬ﺑﻪ ﻋﻨﻮﺍﻥ ﻧﻤﻮﻧﻪ ﺭﻭﺵ ﭘﻴﺸﻨﻬﺎﺩﻱ ﺭﻭﯼ ﺑﺎﻧﮏ ﺍﻃﻼﻋﺎﺕ‬
‫ﺧﺪﻣﺎﺕ ﺗﻠﻔﻦ ﻫﻤﺮﺍﻩ ﺍﻋﻤﺎﻝ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺍﻳﻦ ﺑﺎﻧﮏ ﺍﺯ ﺩﻭ ﺟﺪﻭﻝ ﺗﺸﮑﻴﻞ ﻣﻲ ﺷﻮﺩ‪.‬‬
‫ﺷﮑﻞ ‪) :۲‬ﺍﻟﻒ( ﻗﺴﻤﺘﻲ ﺍﺯ ﺟﺪﻭﻝ ﻣﺮﺑﻮﻁ ﺑﻪ ﻫﺰﻳﻨﻪ ﻣﮑﺎﻟﻤﺎﺕ‪ ‬ﻭ )ﺏ( ﺑﺨﺸﻲ ﺍﺯ ﺟﺪﻭﻝ ﻣﺮﺑﻮﻁ ﺑﻪ ﻗﻄﻊ ﻫﻤﮑﺎﺭﻱ ﻣﺸﺘﺮﻳﺎﻥ ﺑﺎ ﺷﺮﮐﺖ‪ ‬‬
‫ﻣﺮﺣﻠﻪ ‪ -۱‬ﭘﻴﺶ ﭘﺮﺩﺍﺯﺵ‬
‫ﮐﺎﺭ ﭘﻴﺶ ﭘﺮﺩﺍﺯﺵ ﺍﻃﻼﻋﺎﺕ ﺑﺎﻧﮏ ﺷﺎﻣﻞ ﻣﻮﺍﺭﺩ ﺯﻳﺮ ﻣﻲ ﺷﻮﺩ‪:‬‬
‫)ﺍﻟﻒ( ﺣﺬﻑ ﻣﺸﺨﺼﻪﻫﺎﻳﻲ ﺍﺯ ﺟﺪﺍﻭﻝ ﮐﻪ ﺩﺭ ﺟﺮﻳﺎﻥ ﮐﺸﻒ ﺭﻭﺍﺑﻂ ﺑﻲ ﺍﻫﻤﻴﺖ ﻫﺴﺘﻨﺪ‪ .‬‬
‫)ﺏ( ﺗﺒﺪﻳﻞ ﻣﺸﺨﺼﻪﻫﺎ ﺑﻪ ﺻﻮﺭﺕ ﻃﺒﻘﻪﺑﻨﺪﯼ ﺷﺪﻩ‪ .‬ﺑﻪ ﻋﻨﻮﺍﻥ ﻣﺜﺎﻝ ﺷﮑﻞ ‪ ،۳‬ﺟﺪﺍﻭﻝ ﻣﺮﺑﻮﻁ ﺑﻪ ﺑﺎﻧﮏ ﺍﻃﻼﻋﺎﺗﻲ ﺷﺮﮐﺖ ﺧﺪﻣﺎﺕ ﺗﻠﻔﻦ‬
‫ﻫﻤﺮﺍﻩ ﺭﺍ ﭘﺲ ﺍﺯ ﺍﻋﻤﺎﻝ ﻃﺒﻘﻪﺑﻨﺪﯼ ﻧﺸﺎﻥ ﻣﯽﺩﻫﺪ‪.‬‬
‫‪ ‬‬
‫ﺷﮑﻞ ‪ -۳‬ﺗﺒﺪﻳﻞ ﺍﻃﻼﻋﺎﺕ ﺑﻪ ﺻﻮﺭﺕ ﻃﺒﻘﻪﺑﻨﺪﻱ ﺷﺪﻩ‬
‫)ﺝ(‬
‫ﺣﺬﻑ ﻧﻮﻳﺰ ﺍﺯ ﺩﺍﺩﻩﻫﺎ‪ :‬ﺩﺭ ﺍﻳﻦ ﻣﺮﺣﻠﻪ ﺩﺍﺩﻩﻫﺎﻱ ﺧﺎﺭﺝ ﺍﺯ ﻣﺤﺪﻭﺩﻩ ﺍﺯ ﺑﻴﻦ ﺍﻃﻼﻋﺎﺕ ﺗﺸﺨﻴﺺ ﺩﺍﺩﻩ ﺷﺪﻩ ﻭ ﺣﺬﻑ ﻣﻲﮔﺮﺩﻧﺪ‪.‬‬
‫ﺑﺮﺍﯼ ﺍﻳﻦ ﻣﻨﻈﻮﺭ ﻣﻲﺗﻮﺍﻥ ﺍﺯ ﺍﻟﮕﻮﺭﻳﺘﻢﻫﺎﻱ ﻣﻮﺟﻮﺩ ﺑﺮﺍﯼ ﺣﺬﻑ ﻧﻮﻳﺰ ﻧﻈﻴﺮ‪ [Ver02] ‬ﺍﺳﺘﻔﺎﺩﻩ ﮐﺮﺩ‪.‬‬
‫‪ ‬‬
‫‪ Declarative Knowledge ‬‬
‫‪1‬‬
‫)ﺩ( ﻣﺸﺨﺺ ﮐﺮﺩﻥ ﮐﻠﻴﺪﻫﺎﻱ ﺟﺪﻭﻝﻫﺎ ﻭ ﮐﻠﻴﺪﻫﺎﻱ ﺧﺎﺭﺟﻲ ﺑﻪ ﺻﻮﺭﺕ ﻧﻤﺎﺩﻳﻦ‪ :‬ﺑﺮﺍﻱ ﺍﻳﻦ ﻣﻨﻈﻮﺭ ﺑﺎ ﺗﻐﻴﻴﺮ ﻧﺎﻣﮕﺬﺍﺭﻱ ﻭ ﺗﺨﺼﻴﺺ‬
‫ﺍﺳﺎﻣﻲ ﻳﮑﺘﺎ ﺳﻌﻲ ﻣﻲﺷﻮﺩ ﺗﺎ ﺑﻪ ﮐﻠﻴﺪﻫﺎ ﻣﺎﻫﻴﺖ ﻧﻤﺎﺩﻳﻦ ﺩﺍﺩﻩ ﺷﻮﺩ‪ .‬ﺑﻪ ﻋﻨﻮﺍﻥ ﻣﺜﺎﻝ ﺩﻭ ﺟﺪﻭﻝ ﺷﮑﻞ ‪ ۴‬ﺑﻪ ﺟﺪﻭﻝﻫﺎﻱ ﺟﺪﻳﺪﻱ‬
‫ﺗﺒﺪﻳﻞ ﺷﺪﻩﺍﻧﺪ‪ .‬‬
‫ﺷﮑﻞ‪ -۴‬ﺗﺒﺪﻳﻞ ﮐﻠﻴﺪﻫﺎﻱ ﺍﺻﻠﻲ ﻭ ﮐﻠﻴﺪﻫﺎﻱ ﺧﺎﺭﺟﻲ ﺑﻪ ﺍﺳﺎﻣﻲ ﻧﻤﺎﺩﻱ ‪ ‬‬
‫ﻣﺮﺣﻠﻪ ﺩﻭﻡ‪ -‬ﺗﻮﻟﻴﺪ ﻣﺜﺎﻝﻫﺎﻱ ﻣﺜﺒﺖ ﻭ ﻣﻨﻔﻲ ﺍﺯ ﺭﻭﻱ ﺍﻃﻼﻋﺎﺕ ﭘﺎﻳﮕﺎﻩ ﺩﺍﺩﻩ‬
‫ﭘﺲ ﺍﺯ ﺍﻳﻨﮑﻪ ﭘﻴﺶ ﭘﺮﺩﺍﺯﺵ ﻫﺎﻱ ﻻﺯﻡ ﺭﻭﻱ ﺩﺍﺩﻩ ﻫﺎ ﭘﺎﻳﺎﻥ ﭘﺬﻳﺮﻓﺖ‪ ،‬ﺑﺎﻳﺪ ﺍﻃﻼﻋﺎﺕ ﺁﻧﻬﺎ ﺭﺍ ﺑﻪ ﻣﺜﺎﻝﻫﺎﻱ ﻣﺜﺒﺖ ﻭ ﻣﻨﻔﻲ ﺗﺒﺪﻳﻞ ﮐﻨﻴﻢ‪.‬‬
‫)ﺍﻟﻒ( ﺗﻮﻟﻴﺪ ﻣﺜﺎﻟﻬﺎﻱ ﻣﺜﺒﺖ ‪ ‬‬
‫ﻫﺮ ﺭﮐﻮﺭﺩ ﺍﺯ ﺍﻃﻼﻋﺎﺕ ﺟﺪﻭﻝ ﺑﻪ ﻳﮏ ﻣﺜﺎﻝ ﻣﺜﺒﺖ ﺗﺒﺪﻳﻞ ﻣﻲﺷﻮﺩ‪ .‬ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﺍﻳﻨﮑﻪ ﺩﺭ ﻣﺮﺣﻠﻪ ﭘﻴﺶ ﭘﺮﺩﺍﺯﺵ ﻫﻤﻪ ﮐﻠﻴﺪﻫﺎ ﺍﺭﺯﺵ‬
‫ﻧﻤﺎﺩﻳﻦ ﭘﻴﺪﺍ ﮐﺮﺩﻩﺍﻧﺪ‪ ،‬ﻟﺬﺍ ﻧﻴﺎﺯﻱ ﺑﻪ ﺗﺒﺪﻳﻞ ﺭﻭﺍﺑﻂ ﺩﺭ ﺍﻳﻦ ﻣﺮﺣﻠﻪ ﻧﻴﺴﺖ‪ .‬ﺑﻪ ﻋﻨﻮﺍﻥ ﻣﺜﺎﻝ ﺭﮐﻮﺭﺩﻫﺎﻱ ﺟﺪﺍﻭﻝ ﻧﺸﺎﻥ ﺩﺍﺩﻩ ﺷﺪﻩ ﺩﺭ ﺷﮑﻞ ‪۲‬‬
‫ﺑﻪ ﻣﺜﺎﻝﻫﺎﻱ ﻣﺜﺒﺖ ﺍﺭﺍﺋﻪ ﺷﺪﻩ ﺩﺭ ﺷﮑﻞ ‪ ۵‬ﺗﺒﺪﻳﻞ ﻣﻲﺷﻮﺩ‪ .‬‬
‫‪Charge (3824657,High,High,High,Normal,Normal) ‬‬
‫‪Charge (3717191,High,Normal,Normal,High,High) ‬‬
‫‪Ö ‬‬
‫‪Churn(3759999,False) ‬‬
‫‪Churn(3306626,False) ‬‬
‫‪Ö‬‬
‫ﺷﮑﻞ‪ -۵‬ﻣﺜﺎﻝﻫﺎﻱ ﻣﺜﺒﺖ ﺍﺳﺘﺨﺮﺍﺝ ﺷﺪﻩ ﺍﺯ ﺟﺪﻭﻝ ﺑﺎﻧﮏ ﺍﻃﻼﻋﺎﺗﻲ ﺷﺮﮐﺖ ﺧﺪﻣﺎﺕ ﺗﻠﻔﻦ ﻫﻤﺮﺍﻩ‬
‫)ﺏ( ﺳﺎﺧﺖ ﻣﺜﺎﻝﻫﺎﻱ ﻣﻨﻔﻲ‬
‫ﺍﺳﺎﺳﹰﺎ ﺍﻃﻼﻋﺎﺕ ﻣﻮﺟﻮﺩ ﺩﺭ ﭘﺎﻳﮕﺎﻩ ﺩﺍﺩﻩ ﻣﺜﺒﺖ ﺍﺳﺖ ﻭ ﺑﻪ ﺩﻟﻴﻞ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺍﻟﮕﻮﺭﻳﺘﻢﻫﺎﻱ ﻣﺒﺘﻨﻲ ﺑﺮ ‪ rlgg‬ﻧﻴﺎﺯ ﺩﺍﺭﻳﻢ ﺗﺎ ﺭﻭﻳﻪ ﮐﺸﻒ ﺭﻭﺍﺑﻂ‬
‫ﺭﺍ ﺑﺎ ﻣﺜﺎﻝﻫﺎﻱ ﻣﻨﻔﻲ ﻫﺪﺍﻳﺖ ﮐﻨﻴﻢ‪ .‬ﻣﺎ ﺩﺭ ﺭﻭﺵ ﭘﻴﺸﻨﻬﺎﺩﻱ ﺑﺎ ﺭﺟﻮﻉ ﺑﻪ ﺍﻃﻼﻋﺎﺕ ﺑﺎﻧﮏ ﺗﺮﮐﻴﺐﻫﺎﻳﻲ ﺍﺯ ﺩﺍﺩﻩﻫﺎ ﺭﺍ ﭘﻴﺪﺍ ﻣﻲﮐﻨﻴﻢ ﮐﻪ ﺩﺭ‬
‫ﺑﺎﻧﮏ ﺛﺒﺖ ﻧﺸﺪﻩﺍﻧﺪ ﻭ ﺁﻧﻬﺎ ﺭﺍ ﺑﻪ ﻋﻨﻮﺍﻥ ﻣﺜﺎﻝ ﻣﻨﻔﻲ ﻣﻮﺭﺩ ﺍﺳﺘﻔﺎﺩﻩ ﻗﺮﺍﺭ ﻣﻲ ﺩﻫﻴﻢ‪.‬‬
‫ﻣﺮﺣﻠﻪ ﺳﻮﻡ‪-‬ﺍﺟﺮﺍﻱ ﺍﻟﮕﻮﺭﻳﺘﻢ ‪ILP‬‬
‫ﺩﺭ ﺍﻳﻦ ﻣﺮﺣﻠﻪ ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﻳﮏ ﺭﻭﺵ ﻣﺒﺘﻨﻲ ﺑﺮ ﺗﮑﺮﺍﺭ‪ ،‬ﻫﺮ ﺑﺎﺭ ﺗﻤﺎﻣﻲ ﻣﺜﺎﻟﻬﺎﻱ ﻣﺜﺒﺖ ﻭ ﺑﺨﺸﻲ ﺍﺯ ﻣﺜﺎﻟﻬﺎﻱ ﻣﻨﻔﻲ ﺑﻪ ﺍﺑﺰﺍﺭ ‪) ILP‬ﻣﺜﻼ‬
‫‪ (GOLEM‬ﺩﺍﺩﻩ ﻣﻲ ﺷﻮﺩ ﻭ ﻫﺮ ﺑﺎﺭ ﺗﻌﺪﺍﺩﻱ ﻗﺎﻋﺪﻩ ﺑﻪ ﺩﺳﺖ ﻣﻲ ﺁﻳﺪ‪.‬‬
‫ﺑﺎ ﺍﺟﺮﺍﯼ ﺍﻟﮕﻮﺭﻳﺘﻢ ﻣﻨﻄﻖ ﺍﺳﺘﻘﺮﺍﻳﻲ ﺑﻪ ﮐﻤﮏ ﺍﺑﺰﺍﺭ ﻫﺎﻱ ﺫﮐﺮ ﺷﺪﻩ ﺭﻭﻱ ﺟﺪﺍﻭﻝ ﺷﮑﻞ ‪ ۲‬ﻗﻮﺍﻋﺪ ﺯﻳﺮ ﺗﻮﺳﻂ ﺳﻴﺴﺘﻢ ﮐﺸﻒ ﻣﻲﮔﺮﺩﺩ‪:‬‬
‫‪Charge(A,B,High,High,,C,D) ‬‬
‫ﺍﻳﻦ ﻗﺎﻋﺪﻩ ﻧﺸﺎﻥ ﻣﻲ ﺩﻫﺪ ﮐﻪ ﺑﻴﻦ ﻣﺪﺕ ﻣﮑﺎﻟﻤﻪ ﻭ ﻫﺰﻳﻨﻪ ﺁﻥ ﺍﺭﺗﺒﺎﻁ ﻣﺴﺘﻘﻴﻤﻲ ﻭﺟﻮﺩ ﺩﺍﺭﺩ ﻭ ﺍﮔﺮ ﻣﺪﺕ ﻣﮑﺎﻟﻤﻪ ﻃﻮﻻﻧﻲ ﺑﺎﺷﺪ‪ ،‬ﻫﺰﻳﻨﻪ ﺁﻥ‬
‫ﻧﻴﺰ ﺑﺎﻻ ﺧﻮﺍﻫﺪ ﺑﻮﺩ‪.‬‬
‫‪Churn(A,False):-Charge(A,High,B,C,D,E) ‬‬
‫ﺍﻳﻦ ﻗﺎﻋﺪﻩ ﻧﺸﺎﻥ ﻣﻲ ﺩﻫﺪ ﮐﻪ ﺍﻓﺮﺍﺩﻱ ﮐﻪ ﻣﻴﺰﺍﻥ ﺍﺳﺘﻔﺎﺩﻩ ﺁﻧﻬﺎ ﺍﺯ ﺧﺪﻣﺎﺕ ﭘﻴﺎﻡ ﺻﻮﺗﻲ ﺑﺎﻻ ﺑﻮﺩﻩ ﺍﺳﺖ ﺑﻪ ﺍﻳﻦ ﺷﺮﮐﺖ ﻭﻓﺎﺩﺍﺭ ﻣﺎﻧﺪﻩ ﺍﻧﺪ‪ .‬ﺍﻳﻦ‬
‫ﺍﻣﺮ ﻣﻲ ﺗﻮﺍﻧﺪ ﻧﺎﺷﻲ ﺍﺯ ﮐﻴﻔﻴﺖ ﺧﺪﻣﺎﺕ ﭘﻴﺎﻡ ﺻﻮﺗﻲ ﻳﺎ ﻧﺮﺥ ﺍﺭﺯﺍﻥ ﺁﻥ ﻭ ﻳﺎ ﺍﻳﺠﺎﺩ ﻭﺍﺑﺴﺘﮕﻲ ﺩﺭ ﺍﺛﺮ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺁﻥ ﺑﺎﺷﺪ‪.‬‬
‫ﻣﺮﺣﻠﻪ ﭼﻬﺎﺭﻡ‪ -‬ﭘﺎﻻﻳﺶ ﻗﻮﺍﻋﺪ‬
‫ﺟﻬﺖ ﺣﺼﻮﻝ ﺍﻃﻤﻴﻨﺎﻥ ﺍﺯ ﺩﺭﺳﺘﻲ ﻗﻮﺍﻋﺪ ﮐﺸﻒ ﺷﺪﻩ ﺗﻮﺳﻂ ﺍﺑﺰﺍﺭ ‪) ILP‬ﺑﻪ ﻋﻨﻮﺍﻥ ﻣﺜﺎﻝ ‪ ( Golem‬ﻻﺯﻡ ﺍﺳﺖ ﺗﺎ ﻗﻮﺍﻋﺪ ﮐﺸﻒ ﺷﺪﻩ‬
‫ﺩﺭ ﻫﺮ ﻣﺮﺣﻠﻪ ﭘﺎﻻﻳﺶ ﺷﻮﻧﺪ ﻭ ﻣﻮﺍﺭﺩ ﻧﺎﺩﺭﺳﺖ ﺣﺬﻑ ﮔﺮﺩﻧﺪ‪ .‬ﺭﻭﻳﻪ ﭘﺎﻻﻳﺶ ﺑﻪ ﺍﻳﻨﺼﻮﺭﺕ ﺍﻧﺠﺎﻡ ﻣﻲﺷﻮﺩ ﮐﻪ ﺍﺯ ﺭﻭﻱ ﻗﻮﺍﻋﺪ ﻣﻨﻄﻘﻲ‬
.‫ ﺳﭙﺲ ﺑﺎ ﻗﺎﻋﺪﻩ ﻣﻮﺭﺩ ﻧﻈﺮ ﺗﻄﺒﻴﻖ ﺩﺍﺩﻩ ﻣﻲﺷﻮﺩ‬.‫ ﭘﺮﺱ ﻭ ﺟﻮﻫﺎﻱ ﭘﺎﻳﮕﺎﻩ ﺩﺍﺩﻩ ﺍﻳﺠﺎﺩ ﻣﻲ ﺷﻮﺩ ﻭ ﺭﻭﻱ ﺑﺎﻧﮏ ﺍﺟﺮﺍ ﻣﻲﮔﺮﺩﺩ‬،‫ﮐﺸﻒ ﺷﺪﻩ‬
.‫ ﺣﺬﻑ ﻣﻲﺷﻮﺩ‬،‫ﺩﺭ ﺻﻮﺭﺗﻴﮑﻪ ﺍﻳﻦ ﻗﺎﻋﺪﻩ ﺑﺎ ﻧﺘﻴﺠﻪ ﺣﺎﺻﻞ ﺍﺯ ﭘﺮﺱ ﻭ ﺟﻮ ﺗﻄﺒﻴﻖ ﻧﺪﺍﺷﺘﻪ ﺑﺎﺷﺪ‬
:‫ﺑﻪ ﻋﻨﻮﺍﻥ ﻣﺜﺎﻝ ﻓﺮﺽ ﮐﻨﻴﺪ ﺍﺯ ﺭﻭﻱ ﺟﺪﻭﻝ ﻣﺸﺘﺮﻳﺎﻥ ﻗﺎﻋﺪﻩ ﺯﻳﺮ ﺍﺳﺘﺨﺮﺍﺝ ﺷﺪﻩ ﺍﺳﺖ‬
Charge(A,B,C,D,High,High) .‫ﻣﻌﻨﺎﻱ ﺍﻳﻦ ﻗﺎﻋﺪﻩ ﺍﻳﻦ ﺍﺳﺖ ﮐﻪ ﺑﻴﻦ ﺑﺎﻻ ﺑﻮﺩﻥ ﻫﺰﻳﻨﻪ ﻣﮑﺎﻟﻤﺎﺕ ﺑﻴﻦ ﺍﻟﻤﻠﻠﻲ ﻭ ﻃﻮﻻﻧﻲ ﺑﻮﺩﻥ ﺯﻣﺎﻥ ﻣﮑﺎﻟﻤﺎﺕ ﺍﺭﺗﺒﺎﻁ ﻣﺴﺘﻘﻴﻢ ﻭﺟﻮﺩ ﺩﺍﺭﺩ‬
:‫ﺑﺮﺍﻱ ﺍﻃﻤﻴﻨﺎﻥ ﺍﺯ ﺩﺭﺳﺘﻲ ﺍﻳﻦ ﻗﺎﻋﺪﻩ ﭘﺮﺱ ﻭ ﺟﻮﻱ ﺯﻳﺮ ﺭﻭﻱ ﭘﺎﻳﮕﺎﻩ ﺩﺍﺩﻩ ﺍﺟﺮﺍ ﻣﻲﺷﻮﺩ‬
Select distinct IntlMins from Charge where IntlCharge =High
‫ ﺑﺮﮔﺮﺩﺍﻧﺪﻩ ﻣﻲ ﺷﻮﺩ ﮐﻪ ﺍﻳﻦ ﺍﻣﺮ ﻣﻮﻳﺪ ﺍﻳﻦ ﻧﮑﺘﻪ ﺍﺳﺖ ﮐﻪ ﻫﻤﻪ ﺭﮐﻮﺭﺩﻫﺎﻳﻲ ﮐﻪ ﻣﻘﺪﺍﺭ‬High ‫ﺩﺭ ﻧﺘﻴﺠﻪ ﺍﺟﺮﺍﻱ ﺍﻳﻦ ﭘﺮﺱ ﻭ ﺟﻮ ﻣﻘﺪﺍﺭ‬
‫ ﺩﺍﺭﻧﺪ ﺳﺎﻳﺮ ﻗﺎﻋﺪﻩﻫﺎ ﻧﻴﺰ ﺑﻪ ﻫﻤﻴﻦ ﺗﺮﺗﻴﺐ ﺑﺎ ﺍﺟﺮﺍﻱ ﭘﺮﺱ ﻭ‬High ‫ ﺍﺳﺖ ﺩﺭ ﻓﻴﻠﺪ ﻫﺰﻳﻨﻪ ﻣﮑﺎﻟﻤﻪ ﻧﻴﺰ ﻣﻘﺪﺍﺭ‬High ‫ﻓﻴﻠﺪ ﻣﺪﺕ ﻣﮑﺎﻟﻤﻪ ﺁﻧﻬﺎ‬
.‫ﺟﻮ ﺭﻭﻱ ﺑﺎﻧﮏ ﺍﺛﺒﺎﺕ ﻳﺎ ﺭﺩ ﻣﻲﺷﻮﻧﺪ‬
.‫ ﻣﺮﺍﺣﻞ ﺭﻭﺵ ﭘﻴﺸﻨﻬﺎﺩﻱ ﺟﻬﺖ ﮐﺸﻒ ﻗﻮﺍﻋﺪ ﺭﺍ ﻧﺸﺎﻥ ﻣﻲﺩﻫﺪ‬۵‫ﺷﮑﻞ‬
‫ ﻣﺮﺍﺣﻞ ﺭﻭﺵ ﭘﻴﺸﻨﻬﺎﺩﻱ ﺑﺮﺍﻱ ﮐﺸﻒ ﻗﻮﺍﻋﺪ‬-۵ ‫ﺷﮑﻞ‬
‫ ﻭ ﻧﺘﻴﺠﻪﮔﻴﺮﻱ‬ ‫ ﺍﺭﺯﻳﺎﺑﻲ ﺭﻭﺵ ﭘﻴﺸﻨﻬﺎﺩﻱ‬-۵
‫[ ﻭ‬Qui93] C4.5 ‫ ﺭﻭﺵ ﺩﺭﺧﺖ ﺗﺼﻤﻴﻢ‬.‫ﺩﺭ ﺍﻳﻦ ﻣﻘﺎﻟﻪ ﺭﻭﺷﯽ ﺟﻬﺖ ﮐﺸﻒ ﻗﻮﺍﻋﺪ ﺣﺎﮐﻢ ﺑﻴﻦ ﺩﺍﺩﻩﻫﺎﻱ ﭘﺎﻳﮕﺎﻩ ﺩﺍﺩﻩ ﺍﺭﺍﺋﻪ ﺷﺪ‬
‫[ ﺭﻭﻱ ﻳﮏ ﺟﺪﻭﻝ ﮐﺎﺭ ﻣﻲﮐﻨﻨﺪ ﺩﺭ ﺣﺎﻟﻴﮑﻪ ﺩﺭ ﺭﻭﺵ ﭘﻴﺸﻨﻬﺎﺩﻱ ﭼﻨﻴﻦ‬Mor97] ‫[ ﻭ‬Moo02] ‫ﺍﮐﺜﺮ ﺭﻭﺵﻫﺎﻱ ﻣﺸﺎﺑﻪ ﻧﻈﻴﺮ‬
‫ ﮐﻪ ﻧﺴﺒﺖ ﺑﻪ ﺳﺎﻳﺮ‬،‫ ﻳﮑﻲ ﺍﺯ ﺍﻣﺘﻴﺎﺯﺍﺕ ﺭﻭﺵ ﭘﻴﺸﻨﻬﺎﺩﻱ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﻣﻨﻄﻖ ﻣﺮﺗﺒﻪ ﺍﻭﻝ ﺟﻬﺖ ﺑﻴﺎﻥ ﻗﻮﺍﻋﺪ ﻣﻲﺑﺎﺷﺪ‬.‫ﻣﺤﺪﻭﺩﻳﺘﻲ ﻭﺟﻮﺩ ﻧﺪﺍﺭﺩ‬
‫ ﻫﻤﭽﻨﻴﻦ ﺍﻣﮑﺎﻥ ﺗﻌﺮﻳﻒ ﺩﺍﻧﺶ ﭘﻴﺶ ﺯﻣﻴﻨﻪ ﻳﮑﻲ ﺍﺯ ﻣﺤﺎﺳﻦ ﺍﻳﻦ ﺭﻭﺵ ﺑﻪ ﺷﻤﺎﺭ‬.‫ﺭﻭﺵﻫﺎ ﺷﮑﻞ ﻣﻨﺎﺳﺐ ﺗﺮﻱ ﺟﻬﺖ ﺑﻴﺎﻥ ﻧﺘﺎﻳﺞ ﻣﻲﺑﺎﺷﺪ‬
.‫ﺁﻳﺪ‬
‫ﻣﻲ‬
‫ﻣﺮﺍﺟﻊ‬
[Dze96]
[Lav93]
[Log00] [Mit97]
[Moo02] [Mor97]
[Mug95]
[Qui93] [Ver02] Dzeroski S., Inductive Logic Programming and Knowledge Discovery in Databases, Advances in Knowledge Discovery and Data Mining, pp 117ñ152. MIT Press, 1996. Lavra N. and Dzeroski S., Inductive Logic Programming - Techniques and Applications. Prentice Hall, 1993. Loggie W. T. H., Using Inductive Logic Programming to Assist in the Retrieval of Relevant Information from an Electronic Library System, PKDD Workshop, 2000.
Mitchell T., Machine Learning, McGraw-Hill, 1997. Mooney R. J., Melville, P., and Tang, L. R., Relational Data Mining with Inductive Logic Programming for Link Discovery, in proc. Of the NSFNational Science Foundation Workshop on Next Generation Data Mining, Nov 2002.
Morik K., Knowledge Discovery in Database- An Inductive Logic Programming Approach, Foundations of Computer Science: Potential - Theory - Cognition, pp 429-436, 1997. Muggleton S., Inverse entailment and Progol, New Generation Computing, pp. 245ñ286, 1995. Quinlan, J.R. C4.5: programs for machine learning, Morgan Kaufmann, San Mateo, CA, 1993. Verbaeten, S., Identifying mislabeled training examples in ILP classification problems, Proc. of Machine learning conf. of Belgium and the Netherlands, 2002.