Data Analytics and Mining for Dummies

Data Analytics and Mining is often perceived as an extremely tricky task cut out for Data Analysts and Data Scientists having a thorough knowledge encompassing several different domains such as mathematics, statistics, computer algorithms and programming. However, there are several tools available today that make it possible for novice programmers or people with no absolutely no algorithmic or programming expertise to carry out Data Analytics and Mining. One such tool which is very powerful and provides a graphical user interface and an assembly of nodes for ETL: Extraction, Transformation, Loading, for modeling, data analysis and visualization without, or with only slight programming is the KNIME Analytics Platform.

KNIME, or the Konstanz Information Miner, was developed by the University of Konstanz and is now popular with a large international community of developers. Initially KNIME was originally made for commercial use but now it is available as an open source software and has been used extensively in pharmaceutical research since 2006 and also a powerful data mining tool for the financial data sector. It is also frequently used in the Business Intelligence (BI) sector.

KNIME as a Data Mining Tool

KNIME is also one of the most well-organized tools which enables various methods of machine learning and data mining to be integrated. It is very effective when we are pre-processing data i.e. extracting, transforming, and loading data.

KNIME has a number of good features like quick deployment and scaling efficiency. It employs an assembly of nodes to pre-process data for analytics and visualization. It is also used for discovering patterns among large volumes of data and transforming data into more polished/actionable information.

Some Features of KNIME:

• Free and open source
• Graphical and logically designed
• Very rich in analytics capabilities
• No limitations on data size, memory usage, or functionalities
• Compatible with Windows ,OS and Linux
• Written in Java and edited with Eclipse.

A node is the smallest design unit in KNIME and each node serves a dedicated task. KNIME contains graphical, drag-drop nodes that require no coding. Nodes are connected with one’s output being another’s input, as a workflow. Therefore end-to-end pipelines can be built requiring no coding effort. This makes KNIME stand out, makes it user-friendly and make it accessible for dummies not from a computer science background.

KNIME workflow designed for graduate admission prediction

KNIME has nodes to carry out Univariate Statistics, Multivariate Statistics, Data Mining, Time Series Analysis, Image Processing, Web Analytics, Text Mining, Network Analysis and Social Media Analysis. The KNIME node repository has a node for every functionality you can possibly think of and need while building a data mining model. One can execute different algorithms such as clustering and classification on a dataset and visualize the results inside the framework itself. It is a framework capable of giving insights on data and the phenomenon that the data represent.

Some commonly used KNIME node groups include:

• Input-Output or I/O:  Nodes in this group retrieve data from or to write data to external files or data bases.
• Data Manipulation: Used for data pre-processing tasks. Contains nodes to filter, group, pivot, bin, normalize, aggregate, join, sample, partition, etc.
• Views: This set of nodes permit users to inspect data and analysis results using multiple views. This gives a means for truly interactive exploration of a data set.
• Data Mining: In this group, there are nodes that implement certain algorithms (like K-means clustering, Decision Trees, etc.)

Comparison with other tools 

The first version of the KNIME Analytics Platform was released in 2006 whereas Weka and R studio were released in 1997 and 1993 respectively. KNIME is a proper data mining tool whereas Weka and R studio are Machine Learning tools which can also do data mining. KNIME integrates with Weka to add machine learning algorithms to the system. The R project adds statistical functionalities as well. Furthermore, KNIME’s range of functions is impressive, with more than 1,000 modules and ready-made application packages. The modules can be further expanded by additional commercial features.

Sharma Srishti

Srishti Sharma is working as an Assistant Professor in the Department of Computer Science and Engineering at The NorthCap University, India. Prior to joining The NorthCap University, she was a Teaching Research Fellow in Netaji Subhas Institute of Technology (NSIT), Delhi. Her research areas include Natural Language Processing, Soft computing and Biometrics.