Data Mining and Knowledge Discovery via Logic-Based Methods: by Evangelos Triantaphyllou

By Evangelos Triantaphyllou

The significance of getting ef cient and powerful tools for info mining and kn- ledge discovery (DM&KD), to which the current e-book is dedicated, grows on a daily basis and various such tools were constructed in fresh many years. There exists a good number of assorted settings for the most challenge studied by way of facts mining and information discovery, and it appears a really renowned one is formulated when it comes to binary attributes. during this environment, states of nature of the applying zone into consideration are defined through Boolean vectors de ned on a few attributes. that's, by way of facts issues de ned within the Boolean area of the attributes. it's postulated that there exists a partition of this house into periods, which will be inferred as styles at the attributes while simply numerous info issues are recognized, the so-called optimistic and destructive education examples. the most challenge in DM&KD is de ned as nding ideas for spotting (cl- sifying) new information issues of unknown classification, i. e. , identifying which ones are confident and that are unfavourable. In different phrases, to deduce the binary price of 1 extra characteristic, known as the target or classification characteristic. to resolve this challenge, a few equipment were recommended which build a Boolean functionality keeping apart the 2 given units of optimistic and destructive education info issues.

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Solution Statistics When n = 30 and the Total Number of Examples Is Equal to 600. . . . . . . . . . . . . . . . . . . . . . . Continued. . . . . . . . . . . . . . . . . . . . . . . . . 1 Comparison of Sample and Class Sizes for Biopsy and Cancer (from Woman’s Hospital in Baton Rouge, Louisiana, Unpublished Data, 1995). . . . . . . . . . . . . . . . . . . . . . . . 5 History of Monotone Boolean Function Enumeration.

5 Statistical Difference in the Classification Accuracy of the VSM and OCAT/RA1 Approaches. . . . . . . . . . . . . . . . . 6 Data for the Sign Test to Determine the Consistency in the Ranking of the VSM and OCAT/RA1 Approaches. . . . . . . . . . . . 7 Percentage of Documents from the Population that Were Inspected by the Oracle Before an Accuracy of 100% Was Reached. . . . . 1 A Part of the EMG Data Used in This Study.

Solution Statistics When n = 10 and the Total Number of Examples Is Equal to 400. . . . . . . . . . . . . . . . . . Continued. . . . . . . . . . . . . . . . . . . . . . . . . Solution Statistics When n = 30 and the Total Number of Examples Is Equal to 600. . . . . . . . . . . . . . . . . . . . . . . Continued. . . . . . . . . . . . . . . . . . . . . . . . . 1 Comparison of Sample and Class Sizes for Biopsy and Cancer (from Woman’s Hospital in Baton Rouge, Louisiana, Unpublished Data, 1995).

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