KNIME is an open-source, modular framework (or platform) for graphically building and executing workflows and data analysis pipelines from predefined components (called “nodes”). As the heading suggests, it is a platform to automate and integrate different components for machine learning and data mining through its modular data pipelining concept. KNIME contains a collection of nodes. It can create a workflow that can execute locally as well as in the KNIME web portal after deploying the workflow into the server. In simple words, it helps in automating data science.
.
Konstanz Information Miner or KNIME was started as a proprietary product at University of Konstanz. It releases its latest, complete code base under the GPL (General Public License) v3 license, supported by different operating systems. It can integrate varied open-source projects, e.g. machine learning algorithms from Weka, statistics package R project, as well as LIBSVM, JFreeChart, etc.
.
Benefits of using KNIME
One of the most vital benefits of KNIME is that: it does not require coding. It has many in-built functions and modules. Databases, text files, XML, JSON, networks, images, and even Hadoop-based data can be combined within the same workflow. Basically, it is a visual documentation and coding is optional here. It supports both Python and R, within a wrapper script. It can connect to many JDBC-compliant databases.
.
Data Visualization Ability
With a simple drag-and-drop, different visuals can be created. Some of them include: pie chart, histogram, bar chart, scatter plot, box plot, etc. Feature selection is seen in a new light here. After a dataset is uploaded, the features get listed in a tabular format as per their accuracy values. Users can then choose the number of parameters needed to fit the model. Besides, KNIME also suggests a list of classification algorithms which users can leverage to train the data. We can check which algorithm works best (with the help of a bar graph and ROC curve) for the specified model. At last, the selected model is downloaded in PMML format. The trained model can be exported and then saved.
KNIME is thus used to solve a wide range of business problems, starting from ETL, data integration, to advanced analytics and customer segmentation.
.
