Data Mining Tools
Huge amount of data generated every second and it is necessary to have knowledge of different tools that can be utilized to handle this huge data and apply interesting data mining algorithms and visualizations in quick time.
Data Mining is the set of methodologies used in analyzing data from various dimensions and perspectives, finding previously unknown hidden patterns, classifying and grouping the data and summarizing the identified relationships.
The tasks of data mining are twofold:
- Create predictive power using features to predict unknown or future values of the same or other feature — and
- Create a descriptive power, find interesting, human-interpretable patterns that describe the data.
Four most useful data mining techniques:
- Regression (predictive)
- Association Rule Discovery (descriptive)
- Classification (predictive)
- Clustering (descriptive) and there are many more ……
For doing quick analysis on data using any data mining technique it is important to have hands on knowledge of different tools. All the tools mentioned below has its own peculiarity in terms of implementation and each has its own merits. It all boils down to the requirement of task. Most important thing is to know that tools exist which can immensely enhance the efficiency of a data scientist or a student working on some project, so that you can focus more the things that matter that is is gaining useful insights and making projections. It also takes the pain of implementing any standard algorithm from scratch but at the same time gives you the power to modify the code of tool (open source ) as per requirements.
There are many tools apart from mentioned below and I encourage you to check that out as well. The list I have provided are the one that are most common and used widely in leading companies as well as academia. Also most of them are open source == Awesome :)
Comprehensive List of tools for Data Mining
This is very popular since it is a ready made, open source, no-coding required software, which gives advanced analytics. Written in Java, it incorporates multifaceted data mining functions such as data pre-processing, visualization, predictive analysis, and can be easily integrated with WEKA and R-tool to directly give models from scripts written in the former two. Besides the standard data mining features like data cleansing, filtering, clustering, etc, the software also features built-in templates, repeatable work flows, a professional visualisation environment, and seamless integration with languages like Python and R into work flows that aid in rapid prototyping.
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
Python users playing around with data sciences might be familiar with Orange. It is a Python library that powers Python scripts with its rich compilation of mining and machine learning algorithms for data pre-processing, classification, modelling, regression, clustering and other miscellaneous functions. Orange also comes with a visual programming environment and its workbench consists of tools for importing data, and dragging and dropping widgets and links to connect different widgets for completing the workflow.
R is a free software environment for statistical computing and graphics written in C++. R Studio is IDE specially designed for R language.It is one of the leading tools used to do data mining tasks and comes with huge community support as well as packaged with hundreds of libraries built specifically for data mining.
Primarily used for data preprocessing — i.e. data extraction, transformation and loading, Knime is a powerful tool with GUI that shows the network of data nodes. Popular amongst financial data analysts, it has modular data pipe lining, leveraging machine learning, and data mining concepts liberally for building business intelligence reports.
Rattle, expanded to ‘R Analytical Tool To Learn Easily’, has been developed using the R statistical programming language. The software can run on Linux, Mac OS and Windows, and features statistics, clustering, modelling and visualisation with the computing power of R. Rattle is currently being used in business, commercial enterprises and for teaching purposes in Australian and American universities.
TANAGRA is a free open source data mining software for academic and research purposes. It proposes several data mining methods from exploratory data analysis, statistical learning, machine learning and databases area. TANAGRA is more powerful, it contains some supervised learning but also other paradigms such as clustering, factorial analysis, parametric and non parametric statistics, association rule, feature selection and construction algorithms.The main purpose of Tanagra project is to give researchers and students an easy-to-use data mining software, conforming to the present norms of the software development in this domain (especially in the design of its GUI and the way to use it), and allowing to analyse either real or synthetic data.
XLMiner is the only comprehensive data mining add-in for Excel, with neural nets, classification and regression trees, logistic regression, linear regression, Bayes classifier, K-nearest neighbors, discriminant analysis, association rules, clustering, principal components, and more.
XLMiner provides everything you need to sample data from many sources — PowerPivot, Microsoft/IBM/Oracle databases, or spreadsheets; explore and visualize your data with multiple linked charts; preprocess and ‘clean’ your data, fit data mining models, and evaluate your models’ predictive power.
The drawback of XL Miner is that is paid add in for excel but there is 15 day free trial option. The software has great features and its integration in excel makes life easier.
“ The goal is to turn data into information, and information into insight.” — Carly Fiorina ( former executive, president, and chair of Hewlett-Packard Co.)