Data mining is a world itself, which is why it can easily get very confusing. There is an incredible number of data mining tools available in the market. However, while some might be more suitable for handling data mining in Big Data, others stand out for their data visualization features.
As is explained in this article, data mining is about discovering patterns in data and predict trends and behaviours. Simply put, it is the process of converting vasts sets of data into relevant information. There is not much use in having massive amounts of data if we do not actually know what it means.
This process encompasses other fields such as machine learning, database systems, and statistics. Additionally, data mining functions can vary greatly from data cleansing to artificial intelligence, data analytics, regression, clustering, etc. Consequently, many tools are being developed and updated to fulfil these functions and ensure the quality of large data sets (since poor data quality results in poor and irrelevant insights). This article seeks to explain the best options for each function and context. Keep reading to find out our 21 top mining tools!
Table of contents
- What is data mining?
1.2 The Steps of Data Mining Process?
1.3 The difference between data mining and data warehouse
- Data Mining Tools
2.1 R vs Python
2.2 Integrated data mining tools for statistical analysis
2.3 Open-source data mining solutions
2.4 Data mining tools for Big Data
2.5 Small scale solutions for data mining
2.6 Cloud solutions for data mining
2.7 Data Mining tools for neural networks
2.8 Data mining tools for data visualization
Data mining is a process that encompasses statistics, artificial intelligence, and machine learning. By using intelligent methods, this process extracts information from data, making it comprehensive and interpretable. The process of data mining allows discovering patterns and relationships within data sets as well as predict trends and behaviours.
Technological advancements have contributed to faster and easier automated data analysis. The larger and more complex the data sets are, the higher the chances of finding relevant insights. By identifying and understanding meaningful data, organizations can make good use of valuable information to make decisions and achieve the proposed goals.
Data mining can be applied for several purposes, such as market segmentation, trend analysis, fraud detection, database marketing, credit risk management, education, financial analysis, etc. The process of data mining may be split into several steps according to each organization's approach but, in general, it includes the following five steps:
- Identification of the business' requirements according to the established goals;
- Identification of data sources and understanding which data points need to be analyzed;
- Selection and application of modelling techniques;
- Evaluation of the model to ensure it meets the proposed objectives;
- Development of a report presenting the data mining results or implementation of a repeatable data mining process.
Data warehouse is the process of collecting and managing data. It stores data from various sources into one repository and is especially advantageous for operational business systems (e.g. CRM systems). This process occurs before data mining since this one will discover data patterns and relevant information from the stored data.
Data warehouse benefits include: improvement of data quality in source systems; protect data from source system's updates; ability to integrate several sources of data; and data optimization.
As previously mentioned, data mining is an extremely useful and beneficial process that can help organizations develop strategies based on relevant data insights. Data mining crosses many industries (such as insurance, banking, education, media, technology, manufacturing, etc.) and is at the core of analytical efforts.
The process of data mining can consist of different techniques. Among the most prevalent ones are regression analysis (predictive), association rule discovery (descriptive), clustering (descriptive), and classification (predictive). It can be advantageous to have additional knowledge of diverse data mining tools when developing an analysis. However, keep in mind that these tools have distinct ways to operate due to the different algorithms employed in their design.
The growing importance of data mining in a variety of fields resulted in the continuous introduction of new tools and software upgrades to the market. Consequently, choosing the right software becomes a doubtful and complex task. So, before making any rushed decisions, it is crucial to consider the business or the research requirements.
This article gathered the top 21 data mining tools, which are segmented according to seven categories:
- Integrated data mining tools for statistical analysis;
- Open-source data mining solutions;
- Data mining tools for Big Data;
- Small scale solutions for data mining;
- Cloud solutions for data mining;
- Data Mining tools for neural networks;
- Data mining tools for data visualization.
Keep in mind that some of these tools might belong to more than one category. Our selection was made according to the category in which each tool stands out the most. For instance, even though Amazon EMR belongs to cloud-based solutions, it is simultaneously a great tool to handle Big Data. Furthermore, before we move on to the actual tools, we also take the opportunity to briefly explain the difference between the two most popular programming languages for data science: R and Python. Even though both languages are suitable for most data science tasks, it can be hard (especially in the beginning) to know how to choose between both.
Python and R are among the most used programming languages for data science. One is not necessarily better than the other since both options have their strengths and weaknesses. On one hand, R was developed with statistical analysis in mind; on the other hand, Python offers a more generic approach to data science. Further, R is more focused on data analysis and is more flexible to use available libraries. Contrarily, Python's primary objective is deployment and production, and it allows to create models from scratch. Last but not least, R is often integrated to run locally, and Python is integrated with apps. Despite their differences, both languages can handle vast amounts of data and have a wide stack of libraries.
SPSS, SAS, Oracle Data Mining and R are data mining tools with a predominant focus on the statistical side, rather than the more general approach to data mining that Python (for instance) follows. However, unlike the other statistical programs, R is not a commercial integrated solution. Instead, it is open-source.
1. IBM SPSS
SPSS is one of the most popular statistical software platforms. SPSS used to stand for Statistical Package for the Social Sciences, which indicates its original market (the fields of sociology, psychology, geography, economics, etc.). However, IBM acquired the software in 2009, and later, in 2015, SPSS started standing for Statistical Product and Service Solutions. The software's advanced abilities provide a broad library of machine learning algorithms, statistical analysis (descriptive, regression, clustering, etc.), text analysis, integration with big data, and so on. Moreover, SPPS allows the user to improve their SPSS Syntax with Python and R by using specialized extensions.
R is a programming language and an environment for statistical computing and graphics. It is compatible with UNIX platforms, FreeBSD, Linux, macOS, and Windows operating systems. This free software can run a variety of statistical analysis, such as time-series analysis, clustering, and linear and non-linear modelling. Furthermore, it is also defined as an environment for statistical computing since it is designed to provide a coherent system, supplying excellent data mining packages. Overall, R is a great and very complete tool that additionally offers graphical facilities for data analysis and an extensive collection of intermediate tools. It is an open-source solution to statistical software such as SAS and IBM SPSS.
SAS stands for Statistical Analysis System. This tool is an excellent option for tex mining, optimization and data mining. It offers numerous methods and techniques to fulfil several analytic capabilities, which assess the organization's needs and goals. It includes descriptive modelling (helpful to categorize and profile customers), predictive modelling (convenient to predict unknown outcomes), and prescriptive modelling (useful to parse, filter, and transform unstructured data - such as emails - comment fields, books, and so on). Moreover, its distributed memory processing architecture also makes it highly scalable.
4. Oracle Data Mining
Oracle Data Mining (ODB) is part of Oracle Advanced Analytics. This data mining tool provides exceptional data prediction algorithms for classification, regression, clustering, association, attribute importance and other specialized analytics. These qualities allow ODB to retrieve valuable data insights and accurate predictions. Moreover, Oracle Data Mining comprises programmatic interfaces for SQL, PL/SQL, R and Java.
KNIME stands for Konstanz Information Miner. The software follows an open-source philosophy and was first released in 2006. Over recent years it has been often considered a leader software for data science and machine learning platforms, being used in many industries such as banks, life sciences, publishers, and consulting firms. Further, it offers both on-premise and on the cloud connectors, which facilitates moving data between environments. Even though KNIME is implemented in Java, the software also provides nodes so that users can run it in Ruby, Python and R.
Rapid Miner is an open-source data mining tool with seamless integration with both R and Python. It provides advanced analytics by offering numerous products to create new data mining processes. Plus, it has one of the best predictive analysis systems. This open-source is written in Java and can be integrated with WEKA and R-tool. Some of the most valuable features include: remote analysis processing; create and validate predictive models; multiple data management methods available; built-in templates and repeatable workflows; data filtering, merging, and joining.
Orange is a python-based open-source data mining software. It is a great tool for those starting in data mining but also for experts. In addition to its data mining features, orange also supports machine learning algorithms for data modelling, regression, clustering, preprocessing, and so on. Moreover, orange provides a visual programming environment and the ability to drag and drop widgets and links.
Big data refers to a massive amount of data, which can be structured, unstructured or semi-structured. It covers the five V-characteristics: volume, variety, velocity, veracity, and value. Big Data usually involves multiple Terabytes or Petabytes of data. Due to its complexity, it can be tough (not to say impossible) to process data in a single computer. Thus the right software and data storage can be extremely helpful to discover patterns and predict trends. Regarding data mining solutions for big data, these are our top choices:
8. Apache Spark
Apache Spark stands out for its ease of use when handling big data, being one of the most popular tools. It has multiple interfaces available in Java, Python (PySpark), R (SparkR), SQL, Scala and offers over eighty high-level operators, making it possible to write code more quickly. Plus, this tool is complemented by several libraries, such as SQL and DataFrames, Spark Streaming, GrpahX, and MLlib. Apache Spark also attracts attention for its admirable performance, providing a fast data processing and data streaming platform.
9. Hadoop MapReduce
Hadoop is a collection of open-source tools which handles large amounts of data and other computation problems. Even though Hadoop is written in Java, any programming language can be utilized with Hadoop Streaming. MapReduce is a Hadoop implementation and a programming model. It has been a widely adopted solution for executing complex data mining on Big Data. Simply put, it allows users to map and reduce functions that are usually used in functional programming. This tool can perform large join operations across enormous datasets. Furthermore, Hadoop offers various applications such as user activity analysis, unstructured data processing, log analysis, text mining, etc.
Qlik is a platform that addresses analytics and data mining through a scalable and flexible approach. It has an easy-to-use drag and drop interface and responds instantly to modifications and interactions. Additionally, Qlik supports several data sources and seamless integrations with diverse applications formats either through connectors and extensions, built-in app, or sets of APIs. It is also a great tool for sharing relevant analysis by using a centralized hub.
Scikit-learn is a free software tool for machine learning in Python, providing outstanding data mining capabilities and data analysis. It offers a vast number of features such as classification, regression, clustering, preprocessing, model selection and dimension reduction.
12. Rattle (R)
Rattle was developed in the R programming language and is compatible with macOS, Windows, and Linux operating systems. It is mainly used for commercial enterprises and businesses, as well as for scholar purposes (particularly in the United States and Australia). The computing power of R allows this software to provide features like clustering, data visualization, modelling, and other statistical analysis.
13. Pandas (Python)
For data mining in Python Pandas is also a widely known open-source tool. It is a library that stands out for working with data analysis and managing data structures.
H3O is an open-source data mining software used mainly by organizations to analyze data stored in cloud infrastructure. This tool is written in R language but is also compatible with Python for building models. One of the greatest advantages is that H3O allows a fast and easy deployment into production due to Java's language support.
Cloud-based solutions are becoming increasingly necessary for data mining. The implementation of data mining techniques through the cloud allows users to retrieve important information from virtually integrated data warehouses, which reduces the costs of storage and infrastructure.
15. Amazon EMR
Amazon EMR is a cloud solution for processing large amounts of data. Users utilize this tool not only for data mining but also to execute other data science responsibilities such as web indexing, log files analysis, financial analysis, machine learning, etc. This platform uses a variety of open-source solutions (e.g. Apache Spark and Apache Flink) and facilitates scalability in big data environments by automating tasks (for instance, tuning clusters).
16. Azure ML
Azure ML is a cloud-based environment made for building, training and deploying machine learning models. For data mining, Azure ML can perform predictive analysis and allows users to calculate and manipulate data volumes from the cloud platform.
17. Google AI Platform
Similarly to Amazon EMR and Azure ML, Google AI Platform is also cloud-based. This platform provides one of the largest machine learning stacks. Google AI Platform includes several databases, machine learning libraries, and other tools that users can use on the cloud to execute data mining and other data science functions.
Neural Networks consists of assimilating data in the way that the human brain processes information. In other words, our brain has millions of cells (neurons) that process external information and afterwards produce an output. Neural networks follow the same principle and can be used for data mining by turning raw data into relevant information.
Pytorch is a Python package and a deep learning framework based on Torch library. It was initially developed by Facebook's AI Research Lab (FAIR), and it is a very well known tool in Data Science due to its deep neural networks feature. It allows users to perform the data mining steps to program an entire neural network: load data, preprocess data, define a model, train it, and evaluate. Plus, with a strong GPU acceleration, Torch enables a fast array computation. Recently, in September 2020, this library became available to R. The torch for R ecosystem includes torch, torchvision, torchaudio, and other extensions.
Similarly to PyTorch, TensorFlow is also a Python library open-source for machine learning, which Google Brain Team originally developed. It can be used to build deep learning models and has a high focus on deep neural networks. In addition to a flexible ecosystem of tools, TensorFlow also provides other libraries and has a widely popular community where developers can ask questions and share. Despite being a Python library, in 2017, TensorFlow introduced and R interface from RStudio to the TensorFlow API.
Data visualization is the graphical representation of the information extracted from the data mining process. These tools allow users to have a visual understanding of the data insights (trends, patterns and outliers) through graphs, charts, maps, and other visual elements.
Matplotlib is an excellent tool for data visualization in Python. This library allows utilizing interactive figures and creating quality plots (for instance, histograms, scatter plots, 3D plots, and image plots) that can later be customized (styles, axes properties, font, etc.).
ggplot2 is a data visualization tool and one of the most popular R packages. This tool enables users to modify components within a plot with a high level of abstraction. Further, it allows users to build almost any type of graph and improve graphics' quality as well as aesthetics.
To select the most appropriate tool, it is first important to have the business or the research goals well established. It is quite common for developers or data scientists who work on data mining to learn several tools. This can be a challenge but also extremely helpful to extract relevant data insights.
As said before, most data mining tools rely on two principal programming languages: R and Python. Each of these languages provides a complete set of packages and respective libraries for data mining and data science in general. Despite these programming languages' predominancy, integrated statistical solutions (like SAS and SPSS) are still very used by organizations.