/// <summary>
/// Efficient optimization in frequent pattern generation when an FPTree present only a single path
/// Generated frequent are automatically inserted in result (frequent pattern pool).
/// The method enumerates all combinations of node in FPTree fp using efficiently bit operators
/// </summary>
/// <param name="fp">Single path FPtree </param>
/// <param name="given">ItemSet given prefix</param>
private void GenerateCombPattern(FPTree fp, ItemSet given)
{
int bits = fp.Depth;
UInt64 enumerator = 1;
UInt64 combination;
UInt64 max = 1;
int index;
int[] itemsetArray = new int[fp.Depth];
int[] supportArray = new int[fp.Depth];
int betaSupport;
fp.Travel = fp.Root;
for (int i = 0; i < fp.Depth; i++)
{
fp.Travel = fp.Travel.Childs[0];
itemsetArray[i] = fp.Travel.Item;
supportArray[i] = fp.Travel.Count;
}
Array.Reverse(itemsetArray);
Array.Reverse(supportArray);
//Max represent the overflow condition
max = max << bits;
//Our enumerator is represented through a 64 bit integer
while (enumerator < max)
{
// Create a new beta result
ItemSet beta = new ItemSet();
betaSupport = 0;
foreach (int item in given.Items)
{
beta.Add(item);
}
index = 0;
combination = enumerator;
while (combination > 0)
{
if ((combination % 2) == 1)
{
beta.Add(itemsetArray[index]);
if ((betaSupport > supportArray[index]) || (betaSupport == 0))
{
betaSupport = supportArray[index];
}
}
combination = combination >> 1;
index++;
}
enumerator++;
beta.ItemsSupport = betaSupport;
result.Add(beta);
}
}
/// <summary>
/// Frequent pattern extraction method that implement FPGrowth logic using an
/// iterative pattern growth approach
/// </summary>
/// <param name="allTrans">Total list of input transaction</param>
/// <returns>Frequent ItemSets</returns>
public List <ItemSet> ExtractFrequentPattern(List <Transaction> allTrans)
{
// compute absolute support = relative support * # of transactions
absMinSup = (int)System.Math.Ceiling(allTrans.Count * _minSup);
result = new List <ItemSet>();
//We could have used two stacks instead of these lists!!!
List <FPTree> FPTreeList = new List <FPTree>();
List <FPTree> FPTreeListNext = new List <FPTree>();
List <ItemSet> GivenListNow = new List <ItemSet>();
List <ItemSet> GivenListNext = new List <ItemSet>();
FPTree Next;
ItemSet Given;
ItemSet Beta;
List <ItemSet> wholeTransToItemSet = new List <ItemSet>();
foreach (Transaction trans in allTrans)
{
wholeTransToItemSet.Add(trans.Itemset);
}
FPTree StartFPtree = new FPTree();
StartFPtree.SetMinSup(absMinSup);
//Build the first tree on the whole transaction database list
StartFPtree.BuildFirstTree(wholeTransToItemSet);
//Add the first tree to the list of the fptree to process
FPTreeList.Add(StartFPtree);
//Here our given prefix is null
GivenListNow.Add(new ItemSet());
//Looping on each fptree until there are ones to process
while (FPTreeList.Count > 0)
{
for (int j = 0; j < FPTreeList.Count; j++)
{
Next = FPTreeList[j];
Given = GivenListNow[j];
if (!Next.isEmpty())
{
// If the FPTree we are examining is composed of a single path
// we use an optimization based on path node combination which
// arrest current iteration
if (Next.HasSinglePath)
{
GenerateCombPattern(Next, Given);
}
else
{
// Header table sorting
Next.SortHeaderTable();
//Loop on each header table entry
for (int i = 0; i < Next.GetHTSize(); i++)
{
//New beta ItemSet representing a frequent pattern
Beta = new ItemSet();
//Concatenate with items present in the given ItemSet
foreach (int item in Given.Items)
{
Beta.Add(item);
}
Beta.Add(Next.GetHTItem(i));
Beta.ItemsSupport = Next.GetHTFreq(i);
// Add beta to frequent pattern result
result.Add(Beta);
// Here we generate the so called Conditional FPTree using a projection
// of the original database called Conditional Pattern Base
FPTreeListNext.Add(Next.CreateFPtree(i));
// Insert current beta in next given list
GivenListNext.Add(Beta);
}
FPTreeList[j] = null;
GivenListNow[j] = null;
}
}
}
FPTreeList.Clear();
GivenListNow.Clear();
for (int j = 0; j < GivenListNext.Count; j++)
{
FPTreeList.Add(FPTreeListNext[j]);
GivenListNow.Add(GivenListNext[j]);
GivenListNext[j] = null;
FPTreeListNext[j] = null;
}
GivenListNext.Clear();
FPTreeListNext.Clear();
}
return(result);
}
/// <summary>
/// Method that build a new FPTree starting from i-th header table entry
/// </summary>
/// <param name="i">index of header table entry (head of Conditional Pattern Base)</param>
/// <returns>the created FPTree</returns>
public FPTree CreateFPtree(int i)
{
FPNode ResultRootNode = new FPNode();
FPTree Result = new FPTree();
Result.Root = ResultRootNode;
Result.MinSup = _minSup;
// Counting the frequency of single items
Dictionary <int, int> FrequencyItemCounter2 = new Dictionary <int, int>();
Result.FrequencyItemCounter = FrequencyItemCounter2;
FPNode node;
//Obtain a reference to head of the list of the i.th htrecord
node = ht.GetHead(i);
while (node != default(FPNode))
{
int support;
_travel = node;
support = _travel.Count;
_travel = _travel.Parent;
if (_travel.Parent != default(FPNode))
{
while (_travel.Parent != default(FPNode))
{
if (FrequencyItemCounter2.ContainsKey(_travel.Item))
{
FrequencyItemCounter2[_travel.Item] += support;
}
else
{
FrequencyItemCounter2.Add(_travel.Item, support);
}
_travel = _travel.Parent;
}
}
node = node.Next;
}
//Evaluate header table dimension
int HTsize = 0;
foreach (int item in FrequencyItemCounter2.Keys)
{
if (FrequencyItemCounter2[item] >= _minSup)
{
HTsize++;
}
}
HeaderTable newht = new HeaderTable(HTsize);
Result.ht = newht;
//Insertion of frequent items in header table
foreach (KeyValuePair <int, int> coppia in FrequencyItemCounter2)
{
if (coppia.Value >= _minSup)
{
newht.addRecord(coppia.Key, coppia.Value);
}
}
// Removal of non frequent items, sorting and final insertion in the FPTreee
node = ht.GetHead(i);
while (node != default(FPNode))
{
_travel = node;
ItemSet path = new ItemSet();
path.ItemsSupport = _travel.Count;
_travel = _travel.Parent;
if (_travel.Parent != default(FPNode))
{
while (_travel.Parent != default(FPNode))
{
path.Add(_travel.Item);
_travel = _travel.Parent;
}
ItemSet SortedList = new ItemSet();
SortedList.ItemsSupport = path.ItemsSupport;
foreach (int item in path.Items)
{
if (FrequencyItemCounter2[item] >= _minSup)
{
SortedList.Add(item);
}
}
if (SortedList.Items.Count > 0)
{
SortedList.Items.Sort(new ItemSortingStrategy(FrequencyItemCounter2));
if (Result._depth < SortedList.ItemsNumber)
{
Result._depth = SortedList.ItemsNumber;
}
Result.AddItemSetTree(SortedList, ResultRootNode);
}
}
node = node.Next;
}
return(Result);
}