Data Tools: WEKA

WEKA

The Java based, open sourced, and platform independent Waikato Environment for Knowledge Analysis (WEKA) tool, for data preprocessing, predictive data analytics, and facilitation interpretations and evaluation (Dogan & Tanrikulu, 2013; Gera & Goel, 2015; Miranda, n.d.; Xia & Gong, 2014).  It was originally developed for analyzing agricultural data and has evolved to house a comprehensive collection of data preprocessing and modeling techniques (Patel & Donga 2015).  It is a java based machine learning algorithm for data mining tasks as well as text mining that could be used for predictive modeling, housing pre-processing, classification, regression, clustering, association rules, and visualization (WEKA, n.d). Also, WEKA contains classification, clustering, association rules, regression, and visualization capabilities, in particular, the C4.5 decision tree predictive data analytics algorithm (Dogan & Tanrikulu, 2013; Gera & Goel, 2015; Hachey & Grover, 2006; Kumar & Fet, 2011). Here WEKA is an open source data and text mining software tool, thus it is free to use. Therefore there are no costs associated with this software solution.

WEKA can be applied to big data (WEKA, n.d.) and SQL Databases (Patel & Donga, 2015). Subsequently, WEKA has been used in many research studies that are involved in big data analytics (Dogan & Tanrikulu, 2013; Gera & Goel, 2015; Hachey & Grover, 2006; Kumar & Fet, 2011; Parkavi & Sasikumar, 2016; Xia & Gong, 2014). For instance, Barak and Modarres (2015) used WEKA for decision tree analysis on predicting stock risks and returns.

The fact that it has been using in this many research studies is that the reliability and validity of the software are high and well established.  Even in a study comparing WEKA with 12 other data analytics tools, is one of two apps studied that have a classification, regression, and clustering algorithms (Gera & Goel, 2015).

A disadvantage of using this tool is its lack of supporting multi-relational data mining, but if one can link all the multi-relational data into one table, it can do its job (Patel & Donga, 2015). The comprehensiveness of analysis algorithms for both data and text mining and pre-processing is its advantage. Another disadvantage of WEKA is that it cannot handle raw data directly, meaning the data had to be preprocessed before it is entered into the software package and analyzed (Hoonlor, 2011). WEKA cannot even import excel files, data in Excel have to be converted into CSV format to be usable within the system (Miranda, n.d.)

References:

• Dogan, N., & Tanrikulu, Z. (2013). A comparative analysis of classification algorithms in data mining for accuracy, speed and robustness. Information Technology and Management, 14(2), 105-124. doi:http://dx.doi.org/10.1007/s10799-012-0135-7

• Gera, M., & Goel, S. (2015). Data Mining -Techniques, Methods and Algorithms: A Review on Tools and their Validity. International Journal of Computer Applications, 113(18), 22–29.

• Hachey, B., & Grover, C. (2006). Extractive summarization of legal texts. Artificial Intelligence and Law, 14(4), 305. doi:http://dx.doi.org/10.1007/s10506-007-9039-z

• Hoonlor, A. (2011). Sequential patterns and temporal patterns for text mining. UMI Dissertation Publishing.
• Kumar, D., & Fet, D. (2011). Performance Analysis of Various Data Mining Algorithms: A Review. International Journal of Computer Applications, 32(6), 9–16.

• Miranda, S. (n.d.). An Introduction to Social Analytics : Concepts and Methods.
• Parkavi, S. & Sasikumar, S. (2016). Prediction of Commodities Market by Using Data Mining Technique. i-Manager’s Journal on Computer Science.
• Patel, K., & Donga, J. (2015). Practical Approaches: A Survey on Data Mining Practical Tools. Foundations, 2(9).
• WEKA (n.d.) WEKA 3: Data Mining Software in Java. Retrieved from http://www.cs.waikato.ac.nz/ml/weka/
• Xia, B. S., & Gong, P. (2014). Review of business intelligence through data analysis. Benchmarking, 21(2), 300–311. http://doi.org/http://dx.doi.org/10.1108/BIJ-08-2012-0051