| Package | Description | 
|---|---|
| de.lmu.ifi.dbs.elki.algorithm.outlier.subspace | 
 Subspace outlier detection methods. 
 | 
| de.lmu.ifi.dbs.elki.database | 
 ELKI database layer - loading, storing, indexing and accessing data 
 | 
| de.lmu.ifi.dbs.elki.distance.similarityfunction | 
 Similarity functions. 
 | 
| de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel | 
 Kernel functions. 
 | 
| Modifier and Type | Field and Description | 
|---|---|
private SimilarityFunction<V,D> | 
SOD.similarityFunction
The similarity function  
SOD.SIM_ID. | 
private SimilarityFunction<V,D> | 
SOD.Parameterizer.similarityFunction
The similarity function -  
SOD.SIM_ID. | 
| Constructor and Description | 
|---|
SOD(int knn,
   double alpha,
   SimilarityFunction<V,D> similarityFunction)
Constructor with parameters. 
 | 
| Modifier and Type | Method and Description | 
|---|---|
static <O,D extends Distance<D>>  | 
QueryUtil.getSimilarityQuery(Database database,
                  SimilarityFunction<? super O,D> similarityFunction,
                  Object... hints)
Get a similarity query, automatically choosing a relation. 
 | 
<O,D extends Distance<D>>  | 
Database.getSimilarityQuery(Relation<O> relation,
                  SimilarityFunction<? super O,D> similarityFunction,
                  Object... hints)
Get the similarity query for a particular similarity function. 
 | 
<O,D extends Distance<D>>  | 
AbstractDatabase.getSimilarityQuery(Relation<O> objQuery,
                  SimilarityFunction<? super O,D> similarityFunction,
                  Object... hints)  | 
| Modifier and Type | Interface and Description | 
|---|---|
interface  | 
DBIDSimilarityFunction<D extends Distance<D>>
Interface DBIDSimilarityFunction describes the requirements of any similarity
 function defined over object IDs. 
 | 
interface  | 
IndexBasedSimilarityFunction<O,D extends Distance<D>>
Interface for preprocessor/index based similarity functions. 
 | 
interface  | 
NormalizedPrimitiveSimilarityFunction<O,D extends Distance<D>>
Marker interface for similarity functions working on primitive objects, and
 limited to the 0-1 value range. 
 | 
interface  | 
NormalizedSimilarityFunction<O,D extends Distance<?>>
Marker interface to signal that the similarity function is normalized to
 produce values in the range of [0:1]. 
 | 
interface  | 
PrimitiveSimilarityFunction<O,D extends Distance<D>>
Interface SimilarityFunction describes the requirements of any similarity
 function. 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
AbstractDBIDSimilarityFunction<D extends Distance<D>>
Abstract super class for distance functions needing a preprocessor. 
 | 
class  | 
AbstractIndexBasedSimilarityFunction<O,I extends Index,R,D extends Distance<D>>
Abstract super class for distance functions needing a preprocessor. 
 | 
class  | 
AbstractPrimitiveSimilarityFunction<O,D extends Distance<D>>
Base implementation of a similarity function. 
 | 
class  | 
FractionalSharedNearestNeighborSimilarityFunction<O>
SharedNearestNeighborSimilarityFunction with a pattern defined to accept
 Strings that define a non-negative Integer. 
 | 
class  | 
InvertedDistanceSimilarityFunction<O>
Adapter to use a primitive number-distance as similarity measure, by computing
 1/distance. 
 | 
class  | 
Kulczynski1SimilarityFunction
Kulczynski similarity 1. 
 | 
class  | 
Kulczynski2SimilarityFunction
Kulczynski similarity 2. 
 | 
class  | 
SharedNearestNeighborSimilarityFunction<O>
SharedNearestNeighborSimilarityFunction with a pattern defined to accept
 Strings that define a non-negative Integer. 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
FooKernelFunction
Provides an experimental KernelDistanceFunction for NumberVectors. 
 | 
class  | 
LinearKernelFunction<O extends NumberVector<?>>
Provides a linear Kernel function that computes a similarity between the two
 feature vectors V1 and V2 defined by V1^T*V2. 
 | 
class  | 
PolynomialKernelFunction
Provides a polynomial Kernel function that computes a similarity between the
 two feature vectors V1 and V2 defined by (V1^T*V2)^degree. 
 |