pairs01, ..., pairs04.txt ========================= Source: Deutscher Wetter Dienst http://www.dwd.de/bvbw/appmanager/bvbw/dwdwwwDesktop/?_nfpb=true&_pageLabel=_dwdwww_klima_umwelt_klimadaten_deutschland&T82002gsbDocumentPath=Navigation%2FOeffentlichkeit%2FKlima__Umwelt%2FKlimadaten%2Fkldaten__kostenfrei%2Fausgabe__mittelwerte__node.html__nnn%3Dtrue Postprocessing: - merged three datasets, selecting those weather stations which had measurements for Precipitation, Sunshine duration and Temperature - selected six columns (altitude, latitude, longitude; yearly averages of sunshine duration, temperature, precipitation) - converted latitude and longitude to decimal notation Variables: 1. altitude 2. latitude 3. longitude 4. sunshine duration 5. temperature 6. precipitation var 1 var2 ------------------------------------------------------ pairs01.txt: Altitude -> Temperature pairs02.txt: Altitude -> Precipitation pairs03.txt: Longitude -> Temperature pairs04.txt: Sunshine <- Altitude pairs05.txt, ..., pairs07.txt ============================= Source: UCI Machine Learning Repository: Abalone dataset http://archive.ics.uci.edu/ml/datasets/Abalone Variables: Name Data Type Meas. Description ---- --------- ----- ----------- 1 Sex nominal M, F, and I (infant) 2 Length continuous mm Longest shell measurement 3 Diameter continuous mm perpendicular to length 4 Height continuous mm with meat in shell 5 Whole weight continuous grams whole abalone 6 Shucked weight continuous grams weight of meat 7 Viscera weight continuous grams gut weight (after bleeding) 8 Shell weight continuous grams after being dried 9 Rings integer +1.5 gives the age in years var 1 var 2 ------------------------------------------------------ pairs05.txt: Length <- Age (Rings) pairs06.txt: Age (Rings) -> Shell weight pairs07.txt Diameter <- Age (Rings) pairs08.txt: ============ Source: UCI Machine Learning Repository: Census Income dataset http://archive.ics.uci.edu/ml/databases/census-income/census-income.data.html Variables: 1 AAGE age ... 7 AHRSPAY wage per hour ... Selected first 5000 instances for which wage per hour != 0 var 1 var 2 ------------------------------------------------------ pairs08.txt: Age -> Wage per hour Ground truths ============= pairs01.txt: 1 -> 2 pairs02.txt: 1 -> 2 pairs03.txt: 1 -> 2 pairs04.txt: 1 <- 2 pairs05.txt: 1 <- 2 pairs06.txt: 1 -> 2 pairs07.txt: 1 <- 2 pairs08.txt: 1 -> 2