Data Mining

Main Topics Covered for this course such as

  • Find  Mean ,  Median  and Mode ?
  • Measuring  the  Dispersion  of  Data

Solve  the  following  terms :

Quartile Q1   and  Q3

Inter Quartile Range  =  IQR

Five Number Summary  Min , Q1 , Median(Q2) , Q3 , Max

Box plot

Outlier

Histogram Analysis

Q.No.1:

24,25,27,52,47,45,39,40,38,40,36,40,30,68,27,29,35,35,68,35,45,45,52,45,58,59,61,67,61,68,38,25

Arranging Order:

24,25,25,27,27,29,30,35,35,35,36,38,38,39,40,40,40,45,45,45,45,47,52,52,58,59,61,61,67,68,68,68

Min = 24      and   Max =  68 

We make  6 , 7 , 8 classes so let we make  6  class(difference).

Q.No.2: 

 15,13,19,16,21,16,20,22,20,35,33,35,36,40,35,45,35,46,22,52,30,33,23,25,25,25,25,70

Arranging Order:

13 ,  15  , 16 , 16 , 19 , 20 , 20 , 21 , 22 , 22 , 23 , 25 , 25 , 25 , 25 , 30 , 33 , 33 , 35 , 35 , 35, 35 , 36 , 40 , 45, 46 , 52 , 70

Min = 13      and   Max =  70 

We make  6 , 7 , 8 , 10  classes so let we make  10  class(difference).

Coorelation Analysis

Decision  Tree  Induction in Categorical Form

Decision  Tree  Induction by    2nd  Method

Chi – Square Test

Apriori  Algorithm

Q.No.1:    Given   the   following   five  transactions  let the min-support =  60 %  and  min-confidence   =  80%. ?

Q.No.2:    Given   the   following   Nine  transactions  let the min-support =  22 %  and  min-confidence   =  70%. ?

Q.No.3:    Given   the   following   five  transactions  let the min-support =  50 %  and  min-confidence   =  75%. ?

A )  Find     all   Frequent   itemsets    using    Apriori     approaches ?

B )  List   of   all  strong  Association  Rules ?

Naive   Bayes  Classifier

Agglomerative  Clustering Algorithm  Solved
as    Numeric  ( Max and Minimum  Distance )

Agglomerative  Clustering Algorithm  Solved
as    Float   ( Minimum  Distance )

Confusion  Matrices

K – Means   Algorithm

Q. No . 1 :  Following   is the   age  of  10  objects . Perform    one   iteration  of  K – Mean   algorithm   on  this  

data   considering   object   1   and   object   3   as  centroids .   Another   iteration   with   object  1   and  object 

5   as  centroids .   Which    set  of   centroids   gives  better   clusters  . Consider  the  value   of  K  as  2 .

Birch    Algorithm

Q.No.1 :     x1  =  0.5 , x2  =  0.25  , x3 = 0  ,  x4   =   0.65    ,   x5  =  1 ,   x6  =  1.4  ,   x7  =  1.1       

Threshold =  T = 0.15  and   No. of   Entries   in leaf  = L  = 2

Branch   of    Factor  =  B = 2

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