Stuff White People Like #1863

There I sat: sun burning my neck, sweat pouring down my face, watching grown men play at death. I’d been meaning for years to get to Gettysburg to see the reenactment, and this past July, I was lucky enough to be there for the 150th anniversary of the battle. And so there I was, sitting in a grandstand in the middle of a farm in rural Pennsylvania, surrounded by fellow white people, watching a Confederate soldier get shot in the back for pretending to desert in the face of the Union cavalry. He flopped to the ground in front of the grandstand; the announcer gave us paying customers a resounding play-by-play. “They love doing that,” my wife said in my ear, “Very dramatic.” [excerpt

Analysis of monotonicity properties of some rule interestingness measures

One of the crucial problems in the field of knowledge discovery is development of good interestingness measures for evaluation of the discovered patterns. In this paper, we consider quantitative, objective interestingness measures for "if..., then... " association rules. We focus on three popular interestingness measures, namely rule interest function of Piatetsky-Shapiro, gain measure of Fukuda et al., and dependency factor used by Pawlak. We verify whether they satisfy the valuable property M of monotonic dependency on the number of objects satisfying or not the premise or the conclusion of a rule, and property of hypothesis symmetry (HS). Moreover, analytically and through experiments we show an interesting relationship between those measures and two other commonly used measures of rule support and anti-support

Picture words with invisible lines

AbstractA picture word is a word over the alphabet {r, r̄, u, ū, rb, r̄b, ub, ūb}. With any picture word, we associate a picture as follows: the reading of each letter of the word induces a unit move; the letters r and rb (r̄ and r̄b, u and ub, ū and ūb) stand for a right (left, up, down) move; for each letter from {r, r̄, u, ū}, we move by drawing a unit line; for the other letters, we move with “pen-up”. We present a rewriting system S which generates exactly all the picture words describing a given picture