Haliotis rubra: blacklip abalone (ear-shells)

Transcription

1 Dataset 1: Abalone [abalon.xls] Haliotis rubra: blacklip abalone (ear-shells) - edible sea snail - decorative items Dataset 1: Abalone [abalon.xls] Data source: Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and Wes B Ford (1994) "The Population Biology of Abalone (Haliotis species) in Tasmania. I. Blacklip Abalone (_H. rubra from the North Coast and Islands of Bass Strait", Sea Fisheries Division, Technical Report No. 48 (ISSN ) Problem: Estimation of the abalone age from number of rings in abalone shell counting through microscope is very time-consuming task Idea: Predicting the age from physical measurements 1

2 Dataset 1: Abalone [abalon.xls] Data structure Dataset 1: Abalone [abalon.xls] Data mining tasks: The number of rings is the value to predict: either as a continuous value or as a classification problem. 2

3 Dataset 2: Breast cancer [breast.xls] Breast cancer diagnosis Dataset 2: Breast cancer [breast.xls] Data source: - Wisconsin Diagnostic Breast Cancer (WDBC) datast - digitized image of a fine needle aspirate (FNA) of a breast mass 3

4 Dataset 2: Breast cancer [breast.xls] Problem: - Classify cell nuclei present in the image to benign or malignant cell types Idea: - Use automatic computer vision / image analysis classification system Dataset 2: Breast cancer [breast.xls] Data structure Number of cases: 569 Attributes: 1) ID number 2) Diagnosis (M = malignant, B = benign) 3) radius (mean of distances from center to points on the perimeter) 4) texture (standard deviation of gray-scale values) 5) perimeter 6) area 7) smoothness (local variation in radius lengths) 8) compactness (perimeter^2 / area - 1.0) 9) concavity (severity of concave portions of the contour) 10) concave points (number of concave portions of the contour) 11) symmetry 12) fractal dimension ("coastline approximation" - 1) 4

5 Dataset 2: Breast cancer [breast.xls] Data mining tasks: Classify breast cancer cells and make prediction models for cell s radius and area. Dataset 3 : Birth weight [lowbwt.xls] Risk factors associated with birth weight 5

6 Dataset 3 : Birth weight [lowbwt.xls] Data source: Baystate Medical Center, Springfield, Massachusetts, 1986 Problem How external risk factors affect the baby birth weight? Dataset 3 : Birth weight [lowbwt.xls] Data structure Number of Instances: 189 Identification Code: ID Low Birth Weight (0 = Birth Weight > 2500g, l = Birth Weight < 2500g :LOW Age of the Mother in Years: AGE Weight in Pounds at the Last Menstrual Period: LWT Race (1 = White, 2 = Black, 3 = Other): RACE Smoking Status During Pregnancy (1 = Yes, 0 = No): SMOKE History of Premature Labor (0 = None, 1 = One, etc.): PTL History of Hypertension (1 = Yes, 0 = No): HT Presence of Uterine Irritability (1 = Yes, 0 = No): UI Number of Physician Visits During the First Trimester (0 = None, 1 = One, 2 = Two, etc.): FTV Birth Weight in Grams: BWT 6

7 Dataset 3 : Birth weight [lowbwt.xls] Data mining tasks: When does baby classify into a low weight class and what does determine its birth weight? Dataset 4: Squash ripeness [squash.xls] Impact of pre-harvest variables good tasting of squash 7

8 Dataset 4: Squash ripeness [squash.xls] Data source: Winna Harvey Crop & Food Research, Christchurch, New Zealand Problem How pre-harvesting conditions influence the good testing external of squash? Dataset 4: Squash ripeness [squash.xls] Data structure Number of Instances: site - where fruit is located - enumerated 2. daf - number of days after flowering - enumerated 3. fruit - individual number of the fruit (not unique) - enumerated 4. weight - weight of whole fruit in grams - real 5. storewt - weight of fruit after storage - real 6. pene - penetrometer indicates maturity of fruit at harvest integer 7. solids_ - a test for dry matter - integer 8. brix - a refactometer measurement used to indicate sweetnes or ripeness of the fruit - integer 9. a* - the a* coordinate of the HunterLab L* a* b* notation of colour measurement - integer 10. egdd - the heat accumulation above a base of 8c from emergence of the plant to harvest of the fruit - real 11. fgdd - the heat accumulation above a base of 8c from flowering to harvesting - real 12. groundspot_a* - the number indicating colour of skin where the fruit rested on the ground - integer 13. glucose - measured in mg/100g of fresh weight - integer 14. fructose - measured in mg/100g of fresh weight - integer 15. sucrose - measured in mg/100g of fresh weight - integer 16. total - measured in mg/100g of fresh weight - integer 17. glucose+fructos - measured in mg/100g of fresh weight - integer 18. starch - measured in mg/100g of fresh weight - integer 19. sweetness - the mean of eight taste panel scores; out of integer 20. flavour - the mean of eight taste panel scores; out of integer 21. dry/moist - the mean of eight taste panel scores; out of integer 22. fibre - the mean of eight taste panel scores; out of integer 23. heat_input_emerg - the amount of heat emergence after harvest - real 24. heat_input_flower - the amout of heat input before flowering - real Class: 25. Acceptability - the acceptability of the fruit - enumerated 8

9 Dataset 4: Squash ripeness [squash.xls] Data mining tasks: When does effect the acceptability of squashes on the market and what the affect the weight of the quash / brix? Dataset 5: Pasture production [pasture.xls] Predict of pasture production from a variety of biophysical factors 9

10 Dataset 5: Pasture production [pasture.xls] Data source: Dave Barker AgResearch Grasslands, Palmerston, North New Zealand Problem How different management measures affect pasture production? Dataset 5: Pasture production [pasture.xls] Data structure Number of Instances: 36 Ca-Mg - calcium magnesium ration - real LOM - soil lom (g/100g) - real Eworms-No-species - number of spp - integer OM - real legume-yield - kgdm/ha - real root-surface-area - m2/m3 - real Leaf-P - ppm - real pasture-prod-class - pasture production categorisation - enumerated 10

11 Dataset 5: Pasture production [pasture.xls] Data mining tasks: What does effect pasture production and concentration of P in leafs? Dataset 6: Sleep [sleep.xls] Sleep in Mammals 11

12 Dataset 6: Sleep [sleep.xls] Data source: StatLib Problem What does effect sleep in mammals? Dataset 6: Sleep [sleep.xls] Data structure 62 cases body weight in kg brain weight in g maximum life span (years) gestation time (days) predation index (1-5) 1 = minimum (least likely to be preyed upon) 5 = maximum (most likely to be preyed upon) sleep exposure index (1-5) 1 = least exposed (e.g. animal sleeps in a well-protected den) 5 = most exposed overall danger index (1-5) (based on the above two indices and other information) 1 = least danger (from other animals) 5 = most danger (from other animals) total sleep (hrs/day) 12

13 Dataset 6: Sleep [sleep.xls] Data mining tasks: Predict sleep exposure index and total sleep time? Dataset 7: Wine [wine.xls] Wine 13

14 Dataset 7: Wine [wine.xls] Data source: Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno, Genoa, Italy Problem: Is there any difference in chemical properties of three different wine cultivars grown in the same Italian region? Dataset 7: Wine [wine.xls] Data structure 118 cases Class Alcohol Malic acid Ash Alcalinity of ash Magnesium Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity Hue OD280/OD315 of diluted wines Proline (amino acid indicates grape ripeness) 14

15 Dataset 7: Wine [wine.xls] Data mining tasks: Explain differences between three classes of wine and predict the concentration of proline (grape ripeness) in wine? Dataset 8: Soil [soil.xls] Soil fertility 15

16 Dataset 8: Soil [soil.xls] Data source: ECOGEN European FP-5 project Problem: How does tillage affect the soil and how to predict organic matter in studied soil? Dataset 8: Soil [soil.xls] Data structure 480 cases Clay Silt Sand ph (H 2 0) Organic matter K Mg Ca Bt Tillage eu_collembola 16

17 Dataset 8: Soil [soil.xls] Data mining tasks: Make classification model for tillage and predict the amount of organic matter in the soil? Dataset 9: Maize [maize.xls] Maize 17

18 Dataset 9: Maize [maize.xls] Data source: Wastes Wpplication on Soil (WAS) project Problem: How does application of organic wastes influence maize production and accumulation of lead in maize plants? Dataset 9: Maize [maize.xls] Data structure 160 cases Soil properties before wastes application Chemicals properties of wastes Properties of additional soil fertilization Crop production Accumulation of Pb in plants 18

19 Dataset 9: Maize [maize.xls] Data mining tasks: Make classification model for crop production and prediction model for concentration of Pb in plants? 19