Tools for Data Mining
We will learn about some of the commonly used software and tools for data mining, such as Spreadsheets, R-Language, Python, IBM SPSS Statistics, IBM Watson Studio; and SAS.
Spreadsheets, such as Microsoft Excel and Google Sheets, are commonly used for performing basic data mining tasks. Spreadsheets can be used to host data that has been exported from other systems in an easily accessible and easy-to-read format.
You can pivot tables to showcase specific aspects of your data, which is vital when you have huge amounts of data to sort through and analyze. They also make it relatively easier to make comparisons between different sets of data. Add-ins available for Excel, such as the Data Mining Client for Excel, XLMiner, and KnowledgeMiner for Excel, allow you to perform common mining tasks, such as classification, regression, association rules, clustering, and model building.
GoogleSheets also has an array of add-ons that can be used for analysis and mining, such as Text Analysis, Text Mining, Google Analytics.
R is one of the most widely used languages for performing statistical modeling and computations by statisticians and data miners. R is packaged with hundreds of libraries explicitly built for data mining operations such as regression, classification, data clustering, association rule mining, text mining, outlier detection, and social network analysis.
Some of the popular R packages include tm and twitteR. Tm, a framework for text mining applications within R, provides functions for text mining.
TwitteR provides a framework for mining tweets. R Studio is a popularly used open-source Integrated Development Environment (or IDE) for working with the R programming language.
Python libraries like Pandas and NumPy are commonly used for Data Mining. Pandas is an open-source module for working with data structures and analysis. It is possibly one of the most popular libraries for data analysis in Python.
It allows you to upload data in any format and provides a simple platform to organize, sort, and manipulate that data. Using Pandas, you can: perform basic numerical computations such as mean, median, mode, and range; calculate statistics and answer questions regarding the correlation between data and distribution of data; explore data visually and quantitatively; visualize data with help from other Python libraries.
NumPy is a tool for mathematical computing and data preparation in Python. NumPy offers a host of built-in functions and capabilities for data mining. Jupyter Notebooks have become the tool of choice for Data Scientists and Data Analysts when working with Python to perform data mining and statistical analysis.
SPSS stands for Statistical Process for Social Sciences. While the name suggests its original usage in the field of Social Sciences, it is popularly used for advanced analytics, text analytics, trend analysis, validation of assumptions, and translation of business problems into data science solutions.
SPSS is closed source and requires a license for use. SPSS has an easy-to-use interface that requires minimal coding for complex tasks. It comprises efficient data management tools and is popular because of its in-depth analysis capabilities and accurate data results.
In the IBM Cloud Pak for Data, IBM Watson Studio leverages a collection of open source tools such as Jupyter notebooks and extends them with closed source IBM tools that make it a powerful environment for data analysis and data science.
It is available through a web browser on the public cloud, private cloud, and as a desktop app.
Watson Studio enables team members to collaborate on projects ranging from simple exploratory analysis to building machine learning and AI models. It also includes SPSS Modeller flows that enable you to quickly develop predictive models for your business data.
SAS Enterprise Miner is a comprehensive, graphical workbench for data mining. It provides powerful capabilities for interactive data exploration, which enables users to identify relationships within data. SAS can manage information from various sources, mine and transform data, and analyze statistics.
It offers a graphical user interface for non-technical users. With SAS, you can: identify patterns in the data using a range of available modeling techniques; explore relationships and anomalies in data; analyze big data; validate the reliability of findings from the data analysis process.
SAS is very easy to use because of its syntax and is also easy to debug. It has the ability to handle large databases and offers high security to its users.
In this short article, we have learned about just a few of the data mining tools available today. Your decision regarding the best tool for your needs will be driven by the data size and structures the tool supports, the features it offers, its data visualization capabilities, infrastructure needs, ease of use, and learnability. It’s fairly common to use a combination of data mining tools to meet all your needs.
