The following is a set of ten transactions in a convenience store.
|
ID
|
Items
|
|
101
|
Milk, Bread , Cheese
|
|
102
|
Bread , Milk , Sausage, Eggs
|
|
103
|
Eggs , Bread , Cheese
|
|
104
|
Eggs , Milk , Bread, Sausage
|
|
105
|
Bread , Milk , Eggs, Cheese
|
|
106
|
Eggs , Bread , Sausage
|
|
107
|
Bread ,Sausage, Milk
|
|
108
|
Eggs , Bread , Milk, Sausage
|
|
109
|
Eggs , Milk, Cheese
|
|
110
|
Eggs, Bread , Milk
|
Assume that minimum support = 30% and minimum confidence = 50%.
(a) Find all frequent itemsets using the Apriori algorithm. Show clearly your computation steps.
(b) Select the frequent itemset(s) that are of length 3 or more and generate all possible rules from them.
(c) Calculate the confidence of each rule and identify the rules that meet the minimum confidence.
(d) Calculate the values of lift for the strong rules identified in (c ) and sort the rules according to their lift values.
Q2) Data Pre-Processing
In this question you will experiment with data preprocessing in WEKA. You will use the bank-data.arff
In this example, we load the data set into WEKA and then perform a series of pre-processing operations using WEKA's attribute and discretization filters. You will use the GUI interface for WEKA Explorer.Initially (in the Preprocess tab) click "open" and navigate to the directory containing the data file (.csv or .arff). In this case we will open the above data file.
Once the data is loaded, WEKA will recognize the attributes and during the scan of the data will compute some basic statistics on each attribute. The left panel shows the list of recognized attributes, while the top panels indicate the names of the base relation (or table) and the current working relation (which are the same initially). Clicking on any attribute in the left panel will show the basic statistics on that attribute. For categorical attributes, the frequency for each attribute value is shown, while for continuous attributes we can obtain min, max, mean, standard deviation, etc.