static void Main(string[] args)
{
/*
This sample demonstrate how-to use the Apriori algorithm.
As a test data I use a data from an imagine book store:
1) We have a list of authors, which the store sells.
2) We have an information about shopping cart of some users.
As a result we get the following information:
1. Order by popularity of the authors/set of authors.
2. What we can advise the user, basing on his shopping cart. It is ordered by probability (frequent).
*/
TypeRegister.Register<BookAuthor>((a, b) => a.Name.CompareTo(b.Name));
var itemsets = new List<Itemset<BookAuthor>>();
var transactions = new SampleHelper().Transactions.ToList();
var apriori = new Apriori<BookAuthor>
{
MinSupport = (double) 1/9,
SaveItemset = itemset => itemsets.Add(itemset),
GetTransactions = ()=> transactions
};
apriori.ProcessTransactions();
var rules = new List<Rule<BookAuthor>>();
var agrawal = new AgrawalFaster<BookAuthor>
{
MinLift = 0.01,
MinConfidence = 0.01,
TransactionsCount = transactions.Count(),
GetItemsets = ()=>itemsets,
SaveRule = rule=>rules.Add(rule)
};
agrawal.Run();
foreach (var item in itemsets.OrderByDescending(v => v.Support))
{
Console.WriteLine(string.Join("; ", item.Value.OrderBy(i => i.Name)) + " #SUP: " + item.Support);
}
Console.WriteLine("====");
foreach (var item in rules.OrderByDescending(r => r.Confidence))
{
Console.WriteLine(string.Join("; ", item.Combination) + " => " + string.Join("; ", item.Remaining) + " ===> Confidence: " + item.Confidence + " Lift: " + item.Lift);
}
Console.ReadLine();
}
