KNIME (Konstanz Information Miner) is a coherent and comprehensive open source visual platform for data integration, processing, analysis, reporting and exploration. It enables users to visually create data flows (often referred to as pipelines), selectively execute some or all analysis steps, and later investigate the results through interactive views on data and models.
KNIME integrates various components for machine learning and data mining through its modular data pipelining concept. The graphical user interface enables users to assemble nodes for data preprocessing, for modeling and data analysis and visualization.
KNIME is based on the Eclipse Interactive Development Environment and, through its modular API, it is easily extensible.
- Scalability through sophisticated data handling (intelligent automatic caching of data in the background while maximizing throughput performance).
- High, simple extensibility 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.
- Incorporates over 100 processing nodes for data I/O retrieving data from files or databases.
- Preprocessing and cleansing with filtering, group-by, pivoting, binning, normalization, aggregation, joining, sampling, partitioning, and more.
- Data mining:
- Rule induction.
- Decision tree.
- Association rules.
- Naïve bayes.
- Neural networks.
- Support vector machines.
- Various interactive views allowing for interactive data exploration including:
- Box Plot – displays robust statistical parameters: minimum, lower quartile, median, upper quartile, and maximum. These parameters called robust, since they are not sensitive to extreme outliers.
- Conditional Box Plot – partitions the data of a numeric column into classes according to another nominal column and creates a box plot for each of the classes.
- Histogram – displays a histogram view with different viewing options.
- Histogram (interactive) – displays an interactive histogram view with different viewing options. The interactive histogram supports hiliting and the changing of the x axis and aggregation column on the fly.
- Interactive Table – displays data in a table view.
- Lift Chart – used to evaluate a predictive model. The higher the lift (the difference between the “lift” line and the base line), the better performs the predictive model.
- Line Plot – plots the numeric columns of the input table as lines.
- Parallel coordinates – a representation of multi-dimensional information or data, in which multiple dimensions are allocated one-to-one to an equal number of parallel axes on-screen.
- Pie chart – displays a pie chart with different viewing options.
- Pie chart (interactive) – displays an interactive pie chart with different viewing options. The interactive pie chart supports hiliting and the changing of the pie and aggregation column on the fly.
- Scatter Matrix – each matrix element Eij is a scatterplot of the columns i and j, where the values of the i-th column are displayed at the x axis and the values of the j-th column at the y axis while the coordinates are displayed alternating on all sides of the plot.
- Scatter plot – creates a scatterplot of two selectable attributes.
- Integrates analysis modules of the Weka data mining environment.
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