Package de.lmu.ifi.dbs.elki.datasource.parser

Parsers for different file formats and data types The general use-case for any parser is to create objects out of an InputStream (e.g. by reading a data file).

See: Description

Interface Summary

Interface	Description
Parser	A Parser shall provide a ParsingResult by parsing an InputStream.
StreamingParser	Interface for streaming parsers, that may be much more efficient in combination with filters.

Class Summary

Class	Description
AbstractStreamingParser	Base class for streaming parsers.
AbstractStreamingParser.Parameterizer	Parameterization class.
ArffParser	Parser to load WEKA .arff files into ELKI.
ArffParser.Parameterizer	Parameterization class.
BitVectorLabelParser	Parser for parsing one BitVector per line, bits separated by whitespace.
BitVectorLabelParser.Parameterizer	Parameterization class.
CategorialDataAsNumberVectorParser<V extends NumberVector>	A very simple parser for categorial data, which will then be encoded as numbers.
CategorialDataAsNumberVectorParser.Parameterizer<V extends NumberVector>	Parameterization class.
ClusteringVectorParser	Parser for simple clustering results in vector form, as written by ClusteringVectorDumper.
ClusteringVectorParser.Parameterizer	Parameterization class.
CSVReaderFormat	Basic format factory for parsing CSV-like formats.
CSVReaderFormat.Parameterizer	Parameterization class.
LibSVMFormatParser<V extends SparseNumberVector>	Parser to read libSVM format files.
LibSVMFormatParser.Parameterizer<V extends SparseNumberVector>	Parameterization class.
NumberVectorLabelParser<V extends NumberVector>	Parser for a simple CSV type of format, with columns separated by the given pattern (default: whitespace).
NumberVectorLabelParser.Parameterizer<V extends NumberVector>	Parameterization class.
SimplePolygonParser	Parser to load polygon data (2D and 3D only) from a simple format.
SimplePolygonParser.Parameterizer	Parameterization class.
SimpleTransactionParser	Simple parser for transactional data, such as market baskets.
SimpleTransactionParser.Parameterizer	Parameterization class.
SparseNumberVectorLabelParser<V extends SparseNumberVector>	Parser for parsing one point per line, attributes separated by whitespace.
SparseNumberVectorLabelParser.Parameterizer<V extends SparseNumberVector>	Parameterization class.
StringParser	Parser that loads a text file for use with string similarity measures.
StringParser.Parameterizer	Parameterization class.
TermFrequencyParser<V extends SparseNumberVector>	A parser to load term frequency data, which essentially are sparse vectors with text keys.
TermFrequencyParser.Parameterizer<V extends SparseNumberVector>	Parameterization class.

Package de.lmu.ifi.dbs.elki.datasource.parser Description

Parsers for different file formats and data types

The general use-case for any parser is to create objects out of an InputStream (e.g. by reading a data file). The objects are packed in a MultipleObjectsBundle which, in turn, is used by a DatabaseConnection-Object to fill a Database containing the corresponding objects.

By default (i.e., if the user does not specify any specific requests), any KDDTask will use the StaticArrayDatabase which, in turn, will use a FileBasedDatabaseConnection and a NumberVectorLabelParser to parse a specified data file creating a StaticArrayDatabase containing DoubleVector-Objects.

Thus, the standard procedure to use a data set of a real-valued vector space is to prepare the data set in a file of the following format (as suitable to NumberVectorLabelParser):

• One point per line, attributes separated by whitespace.
• Several labels may be given per point. A label must not be parseable as double.
• Lines starting with "#" will be ignored.
• An index can be specified to identify an entry to be treated as class label. This index counts all entries (numeric and labels as well) starting with 0.
• Files can be gzip compressed.

This file format is e.g. also suitable to gnuplot.