Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.itemsetmining |
Algorithms for frequent itemset mining such as APRIORI.
|
de.lmu.ifi.dbs.elki.data |
Basic classes for different data types, database object types and label types.
|
de.lmu.ifi.dbs.elki.data.type |
Data type information, also used for type restrictions.
|
de.lmu.ifi.dbs.elki.datasource.parser |
Parsers for different file formats and data types.
|
de.lmu.ifi.dbs.elki.result |
Result types, representation and handling
|
Modifier and Type | Method and Description |
---|---|
boolean |
SparseItemset.containedIn(BitVector bv) |
boolean |
SmallDenseItemset.containedIn(BitVector bv) |
boolean |
OneItemset.containedIn(BitVector bv) |
abstract boolean |
Itemset.containedIn(BitVector bv)
Test whether the itemset is contained in a bit vector.
|
boolean |
DenseItemset.containedIn(BitVector bv) |
private boolean |
APRIORI.initializeSearchItemset(BitVector bv,
int[] scratchi,
int[] iters)
Initialize the scratch itemset.
|
private boolean |
APRIORI.nextSearchItemset(BitVector bv,
int[] scratchi,
int[] iters)
Advance scratch itemset to the next.
|
Modifier and Type | Method and Description |
---|---|
StringBuilder |
SparseItemset.appendTo(StringBuilder buf,
VectorFieldTypeInformation<BitVector> meta) |
StringBuilder |
SmallDenseItemset.appendTo(StringBuilder buf,
VectorFieldTypeInformation<BitVector> meta) |
StringBuilder |
OneItemset.appendTo(StringBuilder buf,
VectorFieldTypeInformation<BitVector> meta) |
abstract StringBuilder |
Itemset.appendTo(StringBuilder buf,
VectorFieldTypeInformation<BitVector> meta)
Append to a string buffer.
|
StringBuilder |
DenseItemset.appendTo(StringBuilder buf,
VectorFieldTypeInformation<BitVector> meta) |
private FPGrowth.FPTree |
FPGrowth.buildFPTree(Relation<BitVector> relation,
int[] iidx,
int items)
Build the actual FP-tree structure.
|
protected List<SparseItemset> |
APRIORI.buildFrequentTwoItemsets(List<OneItemset> oneitems,
Relation<BitVector> relation,
int dim,
int needed,
DBIDs ids,
ArrayModifiableDBIDs survivors)
Build the 2-itemsets.
|
private DBIDs[] |
Eclat.buildIndex(Relation<BitVector> relation,
int dim,
int minsupp) |
private int[] |
FPGrowth.countItemSupport(Relation<BitVector> relation,
int dim)
Count the support of each 1-item.
|
private StringBuilder |
APRIORI.debugDumpCandidates(StringBuilder msg,
List<? extends Itemset> candidates,
VectorFieldTypeInformation<BitVector> meta)
Debug method: output all itemsets.
|
protected List<? extends Itemset> |
APRIORI.frequentItemsets(List<? extends Itemset> candidates,
Relation<BitVector> relation,
int needed,
DBIDs ids,
ArrayModifiableDBIDs survivors,
int length)
Returns the frequent BitSets out of the given BitSets with respect to the
given database.
|
protected List<SparseItemset> |
APRIORI.frequentItemsetsSparse(List<SparseItemset> candidates,
Relation<BitVector> relation,
int needed,
DBIDs ids,
ArrayModifiableDBIDs survivors,
int length)
Returns the frequent BitSets out of the given BitSets with respect to the
given database.
|
FrequentItemsetsResult |
FPGrowth.run(Database db,
Relation<BitVector> relation)
Run the FP-Growth algorithm
|
FrequentItemsetsResult |
Eclat.run(Database db,
Relation<BitVector> relation)
Run the Eclat algorithm
|
FrequentItemsetsResult |
APRIORI.run(Relation<BitVector> relation)
Performs the APRIORI algorithm on the given database.
|
Modifier and Type | Field and Description |
---|---|
static ByteBufferSerializer<BitVector> |
BitVector.SHORT_SERIALIZER
Serializer for up to 2^15-1 dimensions.
|
Modifier and Type | Method and Description |
---|---|
BitVector |
BitVector.ShortSerializer.fromByteBuffer(ByteBuffer buffer) |
<A> BitVector |
BitVector.Factory.newFeatureVector(A array,
ArrayAdapter<? extends Number,A> adapter) |
<A> BitVector |
BitVector.Factory.newNumberVector(A array,
NumberArrayAdapter<?,? super A> adapter) |
BitVector |
BitVector.Factory.newNumberVector(TIntDoubleMap values,
int maxdim) |
Modifier and Type | Method and Description |
---|---|
ByteBufferSerializer<BitVector> |
BitVector.Factory.getDefaultSerializer() |
Class<? super BitVector> |
BitVector.Factory.getRestrictionClass() |
Modifier and Type | Method and Description |
---|---|
int |
BitVector.ShortSerializer.getByteSize(BitVector vec) |
int |
BitVector.hammingDistance(BitVector v2)
Compute the Hamming distance of two bit vectors.
|
boolean |
BitVector.intersect(BitVector v2)
Compute whether two vectors intersect.
|
int |
BitVector.intersectionSize(BitVector v2)
Compute the vector intersection size.
|
double |
BitVector.jaccardSimilarity(BitVector v2)
Compute the Jaccard similarity of two bit vectors.
|
void |
BitVector.ShortSerializer.toByteBuffer(ByteBuffer buffer,
BitVector vec) |
int |
BitVector.unionSize(BitVector v2)
Compute the vector union size.
|
Modifier and Type | Field and Description |
---|---|
static VectorTypeInformation<BitVector> |
TypeUtil.BIT_VECTOR
Input type for algorithms that require bit vectors.
|
static VectorFieldTypeInformation<BitVector> |
TypeUtil.BIT_VECTOR_FIELD
Input type for algorithms that require bit vector fields.
|
Modifier and Type | Field and Description |
---|---|
(package private) BitVector |
SimpleTransactionParser.curvec
Current vector.
|
Modifier and Type | Field and Description |
---|---|
private VectorFieldTypeInformation<BitVector> |
FrequentItemsetsResult.meta
Metadata of the data relation, for item labels.
|
Constructor and Description |
---|
FrequentItemsetsResult(String name,
String shortname,
List<Itemset> itemsets,
VectorFieldTypeInformation<BitVector> meta)
Constructor.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.