**
One key behind the success of KNIME is its inherent modular workflow approach, which documents and stores the analysis process in the order it was conceived and implemented, while ensuring that intermediate results are always available.
**

Core KNIME features include:

- Scalability through sophisticated data handling (intelligent automatic caching of data in the background while maximizing throughput performance)
- Highly and easily extensible via a well-defined API for plugin extensions
- Intuitive user interface
- Import/export of workflows (for exchanging with other KNIME users)
- Parallel execution on multi-core systems
- Command line version for "headless" batch executions

Available KNIME modules cover a vast range of functionality, such as:

**I/O:**retrieves data from files or data bases**Data Manipulation:**pre-processes your input data with filtering, group-by, pivoting, binning, normalization, aggregation, joining, sampling, partitioning, etc.**Views:**inspects the data and results with several interactive views, supporting interactive data exploration**Hiliting:**ensures hilited data points in one view are also immediately hilited in all other views**Mining:**uses state-of-the-art data mining algorithms like clustering, rule induction, decision tree, association rules, naïve bayes, neural networks, support vector machines, etc. to better understand your data

## Supported Operating Systems

- Windows - 32bit (regularly tested on XP and Vista)
- Windows - 64bit (regularly tested on Vista and verified to work under Windows 7)
- Linux - 32bit (regularly tested on RHEL4/5, OpenSUSE 10.2/10.3/11.0, amongst others)
- Linux - 64bit (regularly tested on RHEL4/5, OpenSUSE 10.2/10.3/11.0, amongst others)
- Mac OSX - with KNIME 2.1 we are also able to release a preliminary, not fully tested, experimental KNIME version for Mac OSX which requires a 64bit Intel-based architecture with Java 1.6

## List of Available Nodes (Modules)

**IO**- Read
*File Reader*- Flexible reader for ASCII files.*ARFF Reader*- Reads ARFF data files.*Table Reader*- Reads table written by the Table Writer node.*PMML Reader*- Reads models from a PMML compliant XML file.*Model Reader*- Reads KNIME model port objects from a file.

- Write
*CSV Writer*- Saves a datatable into an ASCII file.*ARFF Writer*- Writes data into a file in ARFF format.*Table Writer*- Writes a data table to a file using an internal format.*PMML Writer*- Reads a model from a PMML port and writes it into a PMML v3.1 compliant file.*Model Writer*- Writes KNIME model port objects to a file.*XLS Writer*- Saves a datatable into a spreadsheet.

- Artificial Data
*Data Generator*- Creates random data with clusters.

*Cache*- Caches all input data (rows) onto disk for fast access.

- Read
**Database***Database Reader*- Establishes and opens a database access connection from which to read data.*Database Connector*- Creates a database connection.*Database Looping*- Establishes and opens a database access connection from which to read data by a modified, looping WHERE statement.*Database Row Filter*- The Database Row Filter allows to filter rows from database table.*Database Query*- Modifies the input SQL query from a incoming database connection.*Database Column Filter*- The Database Column Filter allows columns to be excluded from the input table database table.*Database Connection Writer*- Writes the input database table into a new database table.*Database Writer*- Establishes and opens a database access connection to which data can be written.*Database Connection Reader*- Reads the entire data from the input database connection.

**Data Manipulation**- Column
- Binning
*Numeric Binner*- Group values of numeric columns categorized string type.*CAIM Binner*- This node implements the CAIM discretization algorithm according to Kurgan and Cios (2004). The discretization is performed with respect to a selected class column.*CAIM Applier*- Takes a binning (discretization) model and a data table as input and bins (discretizes) the columns of the input data according to the model.

- Convert & Replace
*Domain Calculator*- Determines domain information of selected columns.*Number To String*- Converts numbers in a column to strings.*String To Number*- Converts strings in a column to numbers.*Double To Int*- Converts double in a column to integers.*Rename*- Enables you to rename column names or to change their types.*String Replace (Dictionary)*- Replaces the values in a column by matching entries of a dictionary file.*String Replacer*- Replaces values in string cells if they match a certain wildcard pattern.

- Filter
*Column Filter*- The Column Filter allows columns to be excluded from the input table.*Reference Column Filter*- The Reference Column Filter allows columns to be filtered from the first table using the second table as reference.*Low Variance Filter*- Filters out numeric columns, which have a low variance.

- Split & Combine
*Cell Splitter*- Splits cells in one column of the table into separate columns based on a specified delimiter.*Cell Splitter By Position*- Splits cells in one column of the table at fixed positions into separate columns.*Column Combiner*- Combines the content of a set of columns and appends the concatenated string as separate column to the input table.*Create Collection Column*- Combines multiple columns into a new collection column.*Split Collection Column*- Splits a collection column into its sub components, adding one new column for each.*Joiner*- Joins two tables*Regex Split*- Splits an input string (column) into multiple groups according to a regular expression.*Splitter*- Splits the columns of the input table into two output tables.

- Transform
*Case Converter*- This node converts alphanumeric characters to lowercase or UPPERCASE.*Column Comparator*- Compares the cell values of two columns row-wise using different comparison methods. A new column is appended with the result of the comparison.*Column Resorter*- Resorts the order of the columns based on user input*Missing Value*- Filters or replaces missing values in a table.*Normalizer*- Normalizes the attributes of a table.*Normalizer (Apply)*- Normalizes the attributes of a table according to a model.*One2Many*- Transforms the values of one column into appended columns.*Many2One*- Aggregates several columns into one single column.*SMOTE*- Adds artificial data to improve the learning quality using the SMOTE algorithm*Set Operator*- Performs a set operation on two selected table columns.

*HiLite Collector*- Node allows to apply annotations to the set of hilit rows.

- Binning
- Row
- Filter
*HiLite Filter*- Partitions input rows based on their current hilite status.*Nominal Value Row Filter*- Filters rows on nominal attribute value*Numeric Row Splitter*- Node splits the input data according to a given numeric range. The first output port contains the data that matches the criteria, the second the that does not comply with the settings.*Reference Row Filter*- The Reference Row Filter allows rows to be filtered from the first table using the second table as reference.*Row Filter*- Allows filtering of datarows by certain criteria, such as row ID, attribute value, and row number range.*Row Splitter*- Allows splitting of the input table by certain criteria, such as row ID, attribute value, and row number range.

- Transform
*Bitvector Generator*- Generates bitvectors either from a table containing numerical values, or from a string column containing the bit positions to set, hexadecimal or binary strings.*Concatenate*- Concatenates two tables row-wise.*GroupBy*- Groups the table by the selected column(s) and aggregates the remaining columns using the selected aggregation method.*Partitioning*- Splits table into two partitions.*Pivoting*- Node computes an aggregation value between all co-occurrences of two column, pivot and group column. The aggregation can be based on a third numeric column, or just the number of occurrences.*Unpivoting*- Node rotates a number of selected value columns into one single column and at the same time duplicates each single row by the number of selected value columns.*Row Sampling*- Extracts a sample (a bunch of rows) from the input data.*Shuffle*- Shuffles the rows of the input tables.*Sorter*- Sorts the rows according to user-defined criteria.

*RowID*- Node to replace the RowID and/or to create a column with the values of the current RowID.

- Filter
- Matrix
*Transpose*- Transposes a table by swapping rows and columns.

- Column
**Data Views**- Property
*Color Manager*- Assigns colors to a selected nominal or numeric column.*Size Manager*- Assigns sizes corresponding to the values of one numeric column.*Shape Manager*- Assigns shapes to one selected nominal column.*Color Appender*- Assigns an existing color model to a table.*Size Appender*- Appends sizes to one selected column.*Shape Appender*- Appends shapes to one selected column.

- JFreeChart
*Bar Chart*- Displays a bar chart for all columns with nominal values.*Histogram Chart*- Node to display a histogram chart with JFreeChart.*Pie Chart*- Node to display a PieChart with JFreeChart.*XY Chart*- Node to display a XY chart with JFreeChart.

*Box Plot*- A box plot displays robust statistical parameters for numerical attributes and identifies extreme outliers.*Conditional Box Plot*- A box plot displays robust statistical parameters for numerical attributes and identifies extreme outliers. The conditional box plot partitions the data of one column into classes and creates a box plot for each of them.*Histogram*- Displays data in a histogram view. Hiliting is not supported.*Histogram (interactive)*- Displays data in an interactive histogram view with hiliting support.*Interactive Table*- Displays data in a table view.*Lift Chart*- Creates a lift chart*Line Plot*- Plots the numeric columns as lines.*Parallel Coordinates*- Plots the data in Parallel Coordinates.*Pie chart*- Displays data in a pie chart. Hiliting is not supported.*Pie chart (interactive)*- Displays data in an interactive pie chart with hiliting support.*Rule Viewer*- This node visualizes a set of rules that are represented as a table containing numeric support, confidence, lift values and nominal values for the consequence and antecedence.*Scatter Matrix*- Plots a scatter matrix where each column is compared to all others.*Scatter Plot*- Creates a scatterplot of two selected attributes.

- Property
**Statistics**- Regression
*Linear Regression (Learner)*- Performs a multivariate linear regression.*Polynomial Regression Learner*- Learner that builds a polynomial regression model from the input data*Regression (Predictor)*- Predicts the response using a regression model.

*Linear Correlation*- Computes correlation coefficients for pairs of numeric or nominal columns.*Correlation Filter*- Filters out correlated columns.*Statistics*- Calculates statistic moments and counts nominal values and their occurrences across all columns.*Value Counter*- Counts the occurrences of values in a column

- Regression
**Mining**- Association Rules
*Association Rule Learner*- Searches for frequent itemsets with a certain minimum support in a set of bitvectors and optionally generates association rules with a particular confidence from them.*Bitvector Generator*- Generates bitvectors either from a table containing numerical values, or from a string column containing the bit positions to set, hexadecimal or binary strings.

- Bayes
*Naive Bayes Learner*- Creates a naive Bayes model from the given classified data.*Naive Bayes Predictor*- Uses the naive Bayes model from the naive Bayes learner to predict the class membership of each row in the input data.

- Clustering
*Cluster Assigner*- Assigns data to a set of prototypes.*Fuzzy c-Means*- Performs fuzzy c-means clustering.*Hierarchical Clustering*- Performs Hierarchical Clustering.*SOTA Learner*- Clusters numerical data with SOTA.*SOTA Predictor*- Predicts classes for rows using the input SOTA model.*k-Means*- Creates a crisp center based clustering.

- Rule Induction
- Fuzzy Rules
*Fuzzy Rule Learner*- Learns a Fuzzy Rule Model on labeled numeric data.*Fuzzy Rule Predictor*- Applies a Fuzzy Rule Model to numeric data and outputs a prediction for each test instance.

- Fuzzy Rules
- Neural Network
- MLP
*MultiLayerPerceptron Predictor*- Predicts output values based on a trained MLP.*RProp MLP Learner*- Builds and learns an MLP with resilient backpropagation.

- PNN
*PNN Learner (DDA)*- Trains a Probabilistic Neural Network (PNN) on labeled data.*PNN Predictor*- Applies a PNN Model to numeric data and outputs a classification.

- MLP
- Decision Tree
*Decision Tree Learner*- Decision tree induction performed in memory.*Decision Tree Predictor*- Uses an existing decision tree to compute class labels for input vectors.*J48 (Weka)*- Generates an unpruned or pruned C4.5 decision tree (WEKA).

- Misc Classifiers
*K Nearest Neighbor*- Classifies a set of test data based on the k Nearest Neighbor algorithm using the training data.

- MDS
*MDS*- Multi dimensional scaling node, mapping data of a high dimensional space onto a lower dimensional space by applying the Sammons mapping.*MDS Projection*- Multi dimensional scaling node, mapping data of a high dimensional space onto a lower dimensional space by applying a modified Sammons mapping with respect to a given set of fixed points.

- PCA
*PCA*- Principal component analysis*PCA Compute*- Principal component analysis computation*PCA Apply*- Apply principal components projection*PCA Inversion*- Inverse the PCA transformation

- SVM
- LIBSVM
*LIBSVMLearner*- LIBSVM is an integrated software for support vector classification.*LIBSVMPredictor*- Takes a trained LIBSVM to predict the values for new data.

*SVM Learner*- Trains a support vector machine.*SVM Predictor*- This node uses a SVM model generated by the SVM learner node to predict the output for given parameters.

- LIBSVM
- Scoring
*Enrichment Plotter*- Draws enrichment curves*Entropy Scorer*- Scorer for clustering results given a reference clustering.*ROC Curve*- Shows ROC curves*Scorer*- Compares two columns by their attribute value pairs.

- Association Rules
**Chemistry**- CDK
- Translators
*CDK to Molecule*- Converts CDK molecules into various string representations*Molecule to CDK*- Translates various string representations into a specialized CDK type for further processing in CDK based nodes.

*2D Coordinates*- Generates 2D coordinates for CDK cells*3D Viewer*- 3D Molecular Viewer using Jmol.*ChemSpider*- Download molecular structures from ChemSpider.com.*Connectivity*- Handles unconnected molecules*Fingerprints*- Generate fingerprints for CDK molecules*Hydrogen Adder*- Adds hydrogens to CDK molecules*Lipinski's Rule-of-Five*- Calculates the number failures of the Lipinski's Rule Of Five.*Structure Sketcher*- Sketch a molecular structure*Substructure Search*- Filters molecules based on a fragment*XLogP*- Prediction of logP based on the atom-type method called XLogP.*Molecular Properties*- Calculates molecular properties using CDK

- Translators
- I/O
*Mol2 Reader*- Reads molecules from a Mol2 file*Mol2 Writer*- Writes a Mol2 column to a Mol2 file.*Molfile Reader*- Reads molecules from a directory with Molfiles*Molfile Writer*- Writes molecules as Molfiles to a directory*SDF Reader*- Reads molecules from an MDL SDF file*SDF Writer*- Writes molecules to an MDL SDF file*Smiles Reader*- Reads molecules from a directory with Smiles files*Smiles Writer*- Writes molecules as single files to a directory

- Mining
*MoSS*- searches for frequent fragments in a set of molecules (algorithm MoSS v3.13).

- Misc
*SDF Extractor*- Extracts the the various parts from SDF molecules into columns*SDF Inserter*- Inserts properties to SDF/Mol/Ctab strcutures

- Translators
*Molecule Type Cast*- Converts a String column to typed molecule column*OpenBabel*- Converts various molecular file formats into each other

- CDK
**Distance Matrix***Distance Matrix Reader*- Reads triangular or full distance matrix.*Distance Matrix Writer*- Writes column containing distance matrix to file.*Distance Matrix Calculate*- Calculates distance matrix on input table and appends result as (typed) column.*k-Medoids*- Performs k-Medoids algorithm.*Hierarchical Clustering (DistMatrix)*- Performs Hierarchical Clustering on distance matrix input.*Hierarchical Cluster View*- Shows the results of hierarchical clustering.*Hierarchical Cluster Assigner*- Assigns clusters to rows based on an hierarchical clustering

**Image Processing**- IO
*Picture Chooser*- reads images from a directory.

- Preprocessing
*LowPass Filter*- does a LowPass Filtering on the images in the selected column.

- Segmentation
*Binary Image Segmentation*- segments an image based on a binary signal image.*Threshold*- thresholds the input images locally with otsu thresholding.*Voronoi Segmentation*- Voronoi based segmentation

- Features
*Histogram Node*- calculates the histogram features of an image.*Line Node*- calculates the line features of an image.*Texture Node*- calculates the texture features of an image.*Zernike Converter*- Transforms complex numbers into normal double values using the magnitude.*Zernike Node*- calculates the zernike features of an image.*Zernike Reconstructor*- Reconstructs an image based on zernike features.

- Views
*Picture Hilite TableView*- Allows to hilite segments in an image.*RGB Merge*- merges three gray value images into a color image.

- Misc
*IJ Macro*- Executes an ImageJ macro

- IO
**Loop Support**- Cross Validation
*X-Partitioner*- Data partitioner for use in a cross-validation flow*X-Aggregator*- Node that aggregates the result for cross validation.

- Feature Selection
*Backward Feature Elimination Start (1:1)*- Start node for a backward feature elimination loop*Backward Feature Elimination Start (2:2)*- Start node for a backward feature elimination loop*Backward Feature Elimination End*- End node for a backward feature elimination loop*Backward Feature Elimination Filter*- Applies a feature filter model built during backward feature elimination

*Counting Loop Start*- Node at the start of a loop*Generic Loop Start*- Generic loop start node with no termination criterion.*TableRow To Variable Loop Start*- Iterates over an input data table, whereby each row defines on iteration with variable settings taken from the values in that row*Loop End*- Node at the end of a loop*Variable Condition Loop End*- Loop end node that check for a condition in one of the flow variables*Variable Based File Reader*- ASCII file reader from variable locations*Inject Variables (Data)*- Merges Variables from one connection into the data connection. The data is simply handed through.*Inject Variables (Database)*- Merges Variables from one connection into the database connection. The database is simply handed through.*Extract Variables (Data)*- Extracts Variables from a data connection.*Extract Variables (Database)*- Extracts Variables from a Database connection.*TableRow To Variable*- Defines new flow variables based on a single row of the input table and exposes them using a variable connection.*Variable To TableRow*- Extracts variables and puts them into a single row table.*Variable To TableColumn*- Appends one or more variables as new column(s) to the data table.*Interval Loop Start*- Node at the start of a loop

- Cross Validation
**Meta***Feature Elimination*- Backward Feature Elimination*Iterate List of Files*- Iteratively executes the contained flow on a list of files. The list of files needs to be defined by the input table, whereby each row represents one individual file location.*Loop x-times*- Executes the contained workflow multiple times. Aggregation method and termination criteration must be set using the loop start and end node contained in the workflow.*Variables Loop (Data)**Variables Loop (Database)**X-Validation*- Provides a skeleton of nodes necessary for cross validation*Simple Preprocessing**Extended NER Preprocessing**Frequencies**Vector Creation*

**Misc***External SSH Tool*- Executes an external tool on a remote machine via SSH.*Java Snippet*- Calculates a new column based on java code snippets.*External Tool*- Executes an external tool.*Math Formula*- Evaluates mathematical formula, appending result as a new column or replacing an input column.*Rule Engine*- Applies user-defined business rules to the input table

**KNIME Labs**- Neighborgrams
*Universe Marker*- Defines universes (i.e. descriptors) for processing in Neighborgrams or Fuzzy CU Means.*Universe Marker (Apply)*- Applies universe definition as given from Universe Marker node.*NG Construct&View*- Generator and Viewer for Neighborgrams*NG Construct (beta)*- Generator for Neighborgram data structure; suitable for further processing with NG Visual Clustering node.*NG Visual Clusterer (beta)*- Interactive clusterer for neighborgrams, which were constructed with the NG Construct node.*NG Learner (beta)*- Learns a classification model based on Neighborgrams.*NG Predictor (beta)*- Predictor node for Neighborgram classification model.

- Text Processing
- IO
*Dml Document Parser*- Parses dml formatted documents.*Document Grabber*- Downloads and parsers documents.*Flat File Document Parser*- Parses flat ascii files.*PubMed Document Parser*- Parses PubMed search results documents.*Sdml Document Parser*- Parses sdml formatted documents.

- Enrichment
*Abner tagger*- Assigns biomedical named entity tags to terms.*Dictionary tagger*- Assigns tags to named entities specified in the given list.*OpenNLP NE tagger*- Assigs named entity tags, such as "PERSON" or "LOCATION".*POS tagger*- Assigns part of speech tags to terms.

- Transformation
*BoW creator*- Bag of words creator.*Document Data Extractor*- Extracts data from a document into data columns*Document vector*- Creates a document vector for each document.*Sentence Extractor*- Extracts all sentences of a document as string.*String to Term*- Converts strings to terms.*Strings To Document*- Converts the specified strings to documents.*Tags to String*- Converts tags to strings.*Term to String*- Converts terms to strings and adds a new column containing these strings.*Term vector*- Creates a term vector for each term.

- Preprocessing
*Abner Filter*- Filters terms with certain biomedical named entity tags.*Case converter*- Converts terms to lower or upper case.*Dict Replacer*- Replaces whole terms that match with dictionary keys with corresponding specified values.*Hyphenator*- Hyphenates terms / strings.*Kuhlen Stemmer*- Stems terms with the Kuhle stemming algorithm.*Modifiable Term Filter*- Filters terms which are set modifiable or unmodifiable, respectively.*N Chars Filter*- Filters terms consisting of less than N characters.*Number Filter*- Filters term consisting of numbers.*POS Filter*- Filters terms with certain POS tags.*Porter Stemmer*- Stems terms the Porter way.*Punctuation Erasure*- Erases the punctuation characters of terms.*Replacer*- Replaces pattern in terms matching the specified regular expression with the defined replacement.*Snowball Stemmer*- Stems terms with the Snowball stemmer.*Standard Named Entity Filter*- Filters terms with standard named entity tags not specified in the dialog.*Stop word Filter*- Filters terms contained in the stop word file.*Term Grouper*- Groups terms by their text.

- Frequencies
*Frequency Filter*- Filters terms with a certain frequency value.*IDF*- Computes the inverse document frequency (idf) of each term according to the given set of documents and adds a column containing the idf value.*TF*- Computes the relative term frequency (tf) of each term according to each document and adds a column containing the tf value.

- Misc
*Category to class*- Adds a class (string) column to each row, containing the category string of the document in that particular row.*Chi-square keyword extractor*- Extracts relevant keywords from documents.*Document Viewer*- Displays all data of the given documents, like text, authors, publication date and so on.*Keygraph keyword extractor*- Extracts relevant keywords from documents.*String Matcher*- The node finds for each string in the data list the most similar words of the dictionary list.*Tagcloud*- Creates a tag cloud

- IO
*Generic Webservice Client*- Accesses document-style webservices*PPilot Connector*- Accesses Pipeline Pilot web services.

- Neighborgrams
**Time Series***Date Field Extractor*- Extracts date fields from a date/time and appends the values as integer columns.*Time Field Extractor*- Extracts time fields such as and appends the value as integer columns.*Extract Time Window*- Extracts all rows within the specified time window.*Mask Date/Time*- Masks (removes) date or time fields from existing date/time.*Moving Average*- Adds a column with moving average values.*Preset Date/Time*- Presets date or time to timestamps lacking this information.*String to Date/Time*- Parses date and/or time strings into date/time cells.*Time Difference*- Appends the difference between two dates.*Time Generator*- Generates time values*Time to String*- Converts a timestamp column into a column holding strings.

**Python***JPython Function*- Executes a JPython function*JPython Script 1:1*- Executes a JPython script, taking 1 input table and returning 1 output table.*JPython Script 2:1*- Executes a JPython script, taking 2 input tables and returning 1 output table.

**R**- Local
*R Learner (Local)*- Allows execution of R commands in a local R installation and build a R model.*R Predictor (Local)*- Allows to import a R model and predict given data by the use of the model.*R Snippet (Local)*- Allows execution of R commands in a local R installation.*R To PMML (Local)*- Converts a given R object into a corresponding PMML object.*R View (Local)*- Enables the usage of R views using the local R installation.

- Remote
*R Snippet (Remote)*- Allows execution of R commands on an R server. The result of these R commands is returned in the output table of this node. The final result tables' columns are named R1, R2, and so on.*R View (Remote)*- Enables the usage of R views generated on an R server.

- IO
*R Model Reader*- Reads an R model from a file.*R Model Writer*- Writes an R model to a (zip) file.

- Local
**Reporting**- Table Writer
*Table to HTML*- Generates HTML reports out of input data by using the Birt reporting engine.*Table to PDF*- Generates PDF reports out of input data by using the Birt reporting engine.

*to Report*- Provides the incoming data to the KNIME Report Designer.

- Table Writer
**Weka**- Classification Algorithms
- bayes
*AODE*- AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models.*BayesNet*- Bayes Network learning using various search algorithms and quality measures.*ComplementNaiveBayes*- Class for building and using a Complement class Naive Bayes classifier.*HNB*- Contructs Hidden Naive Bayes classification model with high classification accuracy and AUC.*NaiveBayes*- Class for a Naive Bayes classifier using estimator classes.*NaiveBayesMultinomial*- Class for building and using a multinomial Naive Bayes classifier.*NaiveBayesMultinomialUpdateable*- Class for building and using a multinomial Naive Bayes classifier.*NaiveBayesSimple*- Class for building and using a simple Naive Bayes classifier.*NaiveBayesUpdateable*- Class for a Naive Bayes classifier using estimator classes.*WAODE*- WAODE contructs the model called Weightily Averaged One-Dependence Estimators.

- functions
*GaussianProcesses*- Implements Gaussian Processes for regression without hyperparameter-tuning.*IsotonicRegression*- Learns an isotonic regression model.*LeastMedSq*- Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.*LinearRegression*- Class for using linear regression for prediction.*Logistic*- Class for building and using a multinomial logistic regression model with a ridge estimator.*MultilayerPerceptron*- A Classifier that uses backpropagation to classify instances.*PLSClassifier*- A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.*PaceRegression*- Class for building pace regression linear models and using them for prediction.*RBFNetwork*- Class that implements a normalized Gaussian radial basisbasis function network.*SMO*- Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.*SMOreg*- Implements Alex Smola and Bernhard Scholkopf's sequential minimal optimization algorithm for training a support vector regression model.*SVMreg*- SVMreg implements the support vector machine for regression.*SimpleLinearRegression*- Learns a simple linear regression model.*SimpleLogistic*- Classifier for building linear logistic regression models.*VotedPerceptron*- Implementation of the voted perceptron algorithm by Freund and Schapire.*Winnow*- Implements Winnow and Balanced Winnow algorithms by Littlestone.

- lazy
*IB1*- Nearest-neighbour classifier.*IBk*- K-nearest neighbours classifier.*KStar*- K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.*LBR*- Lazy Bayesian Rules Classifier.*LWL*- Locally weighted learning.

- meta
- nestedDichtonomies
*ClassBalancedND*- A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure.*DataNearBalancedND*- A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure.*ND*- A meta classifier for handling multi-class datasets with 2-class classifiers by building a random tree structure.

*AdaBoostM1*- Class for boosting a nominal class classifier using the Adaboost M1 method.*AdditiveRegression*- Meta classifier that enhances the performance of a regression base classifier.*AttributeSelectedClassifier*- Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.*Bagging*- Class for bagging a classifier to reduce variance.*CVParameterSelection*- Class for performing parameter selection by cross-validation for any classifier.*ClassificationViaRegression*- Class for doing classification using regression methods.*CostSensitiveClassifier*- A metaclassifier that makes its base classifier cost-sensitive.*Dagging*- This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.*Decorate*- DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.*END*- A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies.*FilteredClassifier*- Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.*Grading*- Implements Grading. The base classifiers are "graded".*GridSearch*- Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting.*LogitBoost*- Class for performing additive logistic regression.*MetaCost*- This metaclassifier makes its base classifier cost-sensitive.*MultiBoostAB*- Class for boosting a classifier using the MultiBoosting method.*MultiClassClassifier*- A metaclassifier for handling multi-class datasets with 2-class classifiers.*MultiScheme*- Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.*OrdinalClassClassifier*- Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.*RacedIncrementalLogitBoost*- Classifier for incremental learning of large datasets by way of racing logit-boosted committees.*RandomCommittee*- Class for building an ensemble of randomizable base classifiers.*RandomSubSpace*- This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.*RegressionByDiscretization*- A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.*Stacking*- Combines several classifiers using the stacking method.*StackingC*- Implements StackingC (more efficient version of stacking).*ThresholdSelector*- A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.*Vote*- Class for combining classifiers. Different combinations of probability estimates for classification are available.

- nestedDichtonomies
- misc
*FLR*- Fuzzy Lattice Reasoning Classifier (FLR) v5.0*HyperPipes*- Class implementing a HyperPipe classifier.*MinMaxExtension*- This class is an implementation of the minimal and maximal extension.*OLM*- This class is an implementation of the Ordinal Learning Method.*OSDL*- This class is an implementation of the Ordinal Stochastic Dominance Learner.*VFI*- Classification by voting feature intervals.

- trees
*ADTree*- Class for generating an alternating decision tree.*BFTree*- Class for building a best-first decision tree classifier.*DecisionStump*- Class for building and using a decision stump.*Id3*- Class for constructing an unpruned decision tree based on the ID3 algorithm.*J48*- Class for generating an alternating decision tree.*LMT*- Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.*M5P*- M5Base. Implements base routines for generating M5 Model trees and rules.*NBTree*- Class for generating a decision tree with naive Bayes classifiers at the leaves.*REPTree*- Fast decision tree learner.*RandomForest*- Class for constructing a forest of random trees.*RandomTree*- Class for constructing a tree that considers K randomly chosen attributes at each node.*SimpleCART*- Class implementing minimal cost-complexity pruning.*UserClassifier*- Interactively classify through visual means.

- rules
*ConjunctiveRule*- This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.*DecisionTable*- Class for building and using a simple decision table majority classifier.*JRip*- This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER).*M5Rules*- Generates a decision list for regression problems using separate-and-conquer.*NNge*- Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).*OneR*- Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.*PART*- Class for generating a PART decision list.*Prism*- Class for building and using a PRISM rule set for classification.*Ridor*- The implementation of a RIpple-DOwn Rule learner.*ZeroR*- Class for building and using a 0-R classifier.

- bayes
- Cluster Algorithms
*DBScan*- Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.*DensityBasedCluster*- Class for wrapping a Clusterer to make it return a distribution and density.*EM*- Simple EM (expectation maximisation) class.*FarthestFirst*- Cluster data using the FarthestFirst algorithm.*FilteredCluster*- Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.*Optics*- Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Joerg Sander: OPTICS: Ordering Points To Identify the Clustering Structure.*SimpleKMeans*- Cluster data using the k means algorithm.*XMeans*- Cluster data using the X-means algorithm.

- Association Rules
*Apriori*- Class implementing an Apriori-type algorithm.*FilteredAssociator*- Class for running an arbitrary associator on data that has been passed through an arbitrary filter.*GeneralizedSequentialPatterns*- Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set.*PredictiveApriori*- Class implementing the predictive apriori algorithm to mine association rules.*Tertius*- Finds rules according to confirmation measure (Tertius-type algorithm).

- Predictors
*Weka Cluster Assigner*- The Weka Cluster Assigner takes a cluster model generated in a weka node and assigns the data at the inport to the corresponding clusters.*Weka Predictor*- The Weka Predictor takes a model generated in a weka node and classifies the test data at the inport.

- IO
*Weka SerializedClassifier Write*- Takes a trained weka model and writes the weka classifier to a file.*Weka SerializedClassifier Read*- A wrapper around a serialized classifier model.*Weka Classifier Writer*- Writes a weka classification model to a (zip) file.*Weka Classifier Reader*- Reads a weka classification model from a (zip) file.*Weka Clustering Writer*- Writes a weka clustering model to a (zip) file.*Weka Clustering Reader*- Reads a weka clustering model from a (zip) file.

- Classification Algorithms