Comprehensive Analysis of Data Mining Tools

Authors: S. Sarumathi, N. Shanthi

Abstract:

Due to the fast and flawless technological innovation there is a tremendous amount of data dumping all over the world in every domain such as Pattern Recognition, Machine Learning, Spatial Data Mining, Image Analysis, Fraudulent Analysis, World Wide Web etc., This issue turns to be more essential for developing several tools for data mining functionalities. The major aim of this paper is to analyze various tools which are used to build a resourceful analytical or descriptive model for handling large amount of information more efficiently and user friendly. In this survey the diverse tools are illustrated with their extensive technical paradigm, outstanding graphical interface and inbuilt multipath algorithms in which it is very useful for handling significant amount of data more indeed.

Keywords: Classification, Clustering, Data Mining, Machine learning, Visualization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1109307

References:

[1] S. Sarumathi, N. Shanthi, S.Vidhya M. Sharmila. “A Review: Comparative Study of Diverse Collection of Data Mining Tools”. International Journal of Computer, Information, Systems and Control Engineering Vol:8 No:6, 2014
[2] M Ferguson. “Evaluating and Selecting Data Mining Tools”InfoDB, Vol:11 No:2
[3] Data Bionics Research Group, University of Marburg: Databionic esom tools, Website (2006), http://databionic-esom.sourceforge.html/
[4] ELKI: Environment for Developing KDD-Applications Supported by Index-Structures, (online). Available at: http://elki.dbs.ifi.lmu.de/
[5] McCallum, Andrew Kachites. "MALLET: A Machine Learning for Language Toolkit", 2002.
[6] ML-Flex, “Introduction to ML-Flex (online). Available at: http:// mlflex.sourceforge.net/tutorial/index.html
[7] Jason Brownlee, “A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library” 2014.
[8] Soren Sonnenburg et al., “The SHOGUN Machine Learning Toolbox”, Journal of Machine Learning Research 11, 1799-1802, 2010.
[9] M. Wojdyr, J. Appl. Cryst, ” Fityk” 2010.
[10] Tom Schaul, Justin Bayer, Daan Wierstra, Sun Yi, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber, “PyBrain” 2010.
[11] UIMA, “The Apache Software Foundation”,2006.
[12] S. Bird, E. Steven, Edward Loper and Ewan Klein, “Natural Language Processing with Python” O’REILLY.
[13] Steven Bird, Edward Loper, “NLTK: The Natural Language Toolkit” 2002.
[14] Davis E. King, “Dlib C++ Library”, 2015, Dlib (online). Available at: http://dlib.net/
[15] Davis E. King, “Containers”, 2013, Dlib (online). Available at: http://dlib.net/containers.html
[16] Davis E.King, “Image Processing”, 2015, Dlib (online). Available at: http://dlib.net/imaging.html
[17] Davis E.King, “API Wrappers”, 2015, Dlib (online). Available at: http://dlib.net/api.html
[18] Davis E.King, “Graph Tools”, 2013, Dlib (online). Available at: http://dlib.net/graph tools.html
[19] Davis E.King, “Machine Learning”, 2015, Dlib (online). Available at: http://dlib.net/ml.html
[20] Jubatus (online). Available at: http://www.predictiveanalyticstoday.com/ top-40-free-data-mining-software/
[21] Jubatus, PFN & NTT, 2011. (online). Available at: http://jubat.us/en/overview.html
[22] Satoshi Oda, Kota Uenishi, and Shingo Kinoshita,” Jubatus: Scalable Distributed Processing Framework for Realtime Analysis of Big Data”, NTT Technical Review.
[23] SCaVis, Scavis community, 2014.
[24] Cho Ok-Hyeong, “CMSR Data Miner”,2014.
[25] CMSR Data Miner Data Mining & Predictive Modeling Software, Rosella Predictive Knowledge and Data Mining,2005
[26] Vowpal Wabbit: Fast Learning on Big Data, n13,2014