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Monthly Archives: July 2020
NNET in R
https://stackoverflow.com/questions/25876455/neural-network-using-r-nnet-package-nas-when-using-size-2
Posted in Uncategorized
Influential Measures
https://cran.r-project.org/web/packages/olsrr/vignettes/influence_measures.html
Cook’s distance
https://cran.r-project.org/web/packages/olsrr/vignettes/influence_measures.html
Posted in Uncategorized
Linear Regression: Influential Observations
https://www.youtube.com/watch?v=fJSXS4oVf88
Posted in Data Mining, Uncategorized
Confusion matrix
https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/#:~:text=A%20confusion%20matrix%20is%20a,the%20true%20values%20are%20known.&text=The%20classifier%20made%20a%20total,the%20presence%20of%20that%20disease).
https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62
Posted in Data Mining, Data Mining
Same Rank Issue
https://www.statisticshowto.com/spearman-rank-correlation-definition-calculate/
Posted in Uncategorized
Symmetry and Assymetry Data
https://elentra.healthsci.queensu.ca/assets/modules/types-of-data/symmetrical_and_asymmetrical_data.html
Posted in Uncategorized
Absolute and Relative Frequency
https://www.geeksforgeeks.org/absolute-and-relative-frequency-in-r-programming/
Posted in Uncategorized
Exercise 11: Problem solved: Data Mining
code for exercise :
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 |
#a library(arules) data(Groceries) Groceries #b myrules=apriori(Groceries) inspect(myrules) #c myrules=apriori(Groceries,parameter=list(supp=0.05,conf=0.05)) #d inspect(myrules) myrules=apriori(Groceries,parameter=list(minlen=2)) #e myrules=apriori(Groceries,parameter=list(minlen=1)) inspect(myrules) #f myrules=apriori(Groceries,parameter=list(supp=0.01,conf=0.01,minlen=3)) #g inspect(myrules[20]) #answer lift value: Used to judge the strength of the association rule #if lift value is greater than> 1 some usefulness in that rule #the larger the lift is the greater is the strength of the association #https://www.dataminingapps.com/2017/04/what-is-the-lift-value-in-association-rule-mining/ #h cc=sort(myrules,by="lift") inspect(cc[1:10]) #i hh=subset(myrules,subset=lhs %pin% "pork" & lift>1.2) #pin is using as matching function to match character inspect(hh) |
Posted in Data Mining
Exercise 10: Solving for problem
K={1, 1.1, 5, 5.1, 1.5, 5.2, 7.9, 1.2, 8.1, 9}
Total item=10
iter1:
m1=5 m2=9
K1={1, 1.1, 5, 5.1, 1.5, 5.2, 1.2} K2={7.9, 8.1, 9}
m1=2.87==approx(3) m2=8.333==approx(9)
iter(2):
K1={1, 1.1, 5, 5.1, 1.5, 5.2, 1.2} ; K2={7.9, 8.1, 9}
m1=approx(3) m2=approx(9)
So, here same mean twice. so we have to stop.
Posted in Data Mining, Data Mining
Data Mining: Cluster analysis doing manually chapter 10
K-means clustering Algorithm for manually finding from observation:
Step 1: Take mean value
Step 2: Find nearest number of mean and put in cluster
Step 3: Repeat one and two until we get same mean
K={2,3,4,10,11,12,20,25,30}
k=2 [it means we have to create 2 clusters]
iter1:
m1=4 m2=12
k1={2,3,4} [according to nearest distance of 4]
so mean m1=3
k2={10,11,12,20,25,30}
m2=108/6=18
iter2:
k1={2,3,4,10}
m1=4.75==approc(5)
k2={11,12,20,25,30}
m2=19.6==approx(20)
Iter 3:
K1={2,3,4,10,11,12} K2={20,25,30}
m1=7 m2=25
k1={2,3,4,10,11,12} k2={20,25,30}
m1=7, m2=25
Same mean twice. Thus we are getting same mean we have to stop.
Posted in Data Mining, Data Mining