What Is Data Mining? Definition, Techniques, and Tools

Data mining is defined as the process of filtering, sorting, and classifying data from larger datasets to reveal subtle patterns and relationships, which helps enterprises identify and solve complex business problems through data analysis. This article explains data mining in detail, its techniques, and the top 10 data mining tools that are popular in 2022.

What Is Data Mining?

Data mining refers to filtering, sorting, and classifying data from larger datasets to reveal subtle patterns and relationships, which helps enterprises identify and solve complex business problems through data analysis. Data mining software tools and techniques allow organizations to foresee future market trends and make business-critical decisions at crucial times.

Data mining is an essential component of data science that employs advanced data analytics to derive insightful information from large volumes of data. If we dig deeper, data mining is a crucial ingredient of the knowledge discovery in databases (KDD) process, where data gathering, processing, and analysis takes place at a fundamental level.

Businesses rely heavily on data mining to undertake analytics initiatives in the organizational setup. The analyzed data sourced from data mining is used for varied analytics and business intelligence (BI) applications, which consider real-time data analysis along with some historical pieces of information.

With top-notch data mining practices, enterprises can make several business strategies and manage their operations better. This can entail refining customer-centric functions, including advertising, marketing, sales, customer support, finance, HR, etc.

Data mining also plays a vital role in handling business-critical use cases such as cybersecurity planning, fraud detection, risk management, and several others. Data mining finds applications across industry verticals such as healthcare, scientific research, sports, governmental projects, etc.

How does data mining work?

Data mining is predominantly handled by a group of data scientists, skilled BI professionals, analytics groups, business analysts, tech-savvy executives, and personnel having a solid background and inclination toward data analytics.

Fundamentally, machine learning (ML), artificial intelligence (AI), statistical analysis, and data management are crucial elements of data mining that are necessary to scrutinize, sort, and prepare data for analysis. Top ML algorithms and AI tools have enabled the easy mining of massive datasets, including customer data, transactional records, and even log files picked up from sensors, actuators, IoT devices, mobile apps, and servers.

Key stages involved in the data mining process:

• • Data gathering: Data mining begins with the data gathering step, where relevant information is identified, collected, and organized for analysis. Data sources can include data warehouses, data lakes, or any other source that contains raw data in a structured or unstructured format.

• • Data preparation: In the second step, fine-tuning the gathered data is the prime focus. This involves several processes, such as data pre-processing, data profiling, and data cleansing, to fix any data errors. These stages are essential to maintain data quality before following up with the mining and analysis processes.

• • Mining the data: In the third step, the data professional selects an appropriate data mining technique once the desired quality of data is prepared. Here, a proper set of data processing algorithms are identified where sample data is trained initially before running it over the entire dataset.

• • Data analysis and interpretation: In the last step, the results derived in the third step are used to develop analytical models for making future business decisions. Moreover, the data science team communicates the results to the concerned stakeholders via data visualizations and other more straightforward techniques. The information is conveyed in a manner that makes the content digestible for any non-expert working outside the field of data science.

Benefits of data mining

Data mining is beneficial for most businesses primarily because it can run through vast volumes of data and identify hidden patterns, relationships, and trends. The results are helpful for predictive analytics that help in strategic planning while keeping a stock of the current business scenario.

Benefits of data mining for enterprises:

• • Targeted marketing & advertisements: Data mining allows marketing teams to comprehend customer behavior and preferences better. It will enable them to direct targeted advertisements to respective customers showing a pattern of behavior. Moreover, the sales department benefits from data mining as it helps them target customers with a particular inclination toward specific products. It additionally allows them to sell more services and products to older customers.
  • Identifying customer service issues: Data mining is an effective tool to keep track of customer service issues when customers interact with contact center agents through calls and online chats. It gives them a chance to provide better customer service, thanks to the in-depth analysis possible through data mining.
  • Improved supply chain management (SCM): With data mining, businesses can identify market trends and predict future customer behavior that can impact product demand. This allows enterprises to plan for the future and manage the supply of goods and services to meet market demands. Moreover, SCM managers can plan their logistic operations accordingly, streamline product distribution, and optimize warehousing services.
  • Maintaining production uptime: Gathering and mining data from sensors, IoT devices, manufacturing machines, and industrial equipment aids in creating predictive maintenance applications that determine potential problems before the actual incident hurts the machinery. Such pre-timed warnings reduce the unscheduled downtime for machines, thereby boosting overall productivity.
  • Better assess risks: Data mining allows risk managers and concerned business personnel to assess better the risks related to finances, legal matters, or cybersecurity factors that the company may encounter in the future. It gives them the chance to properly prepare for such events and have a plan in place to manage such mishaps better.
  • Drive cost savings: Data mining can easily identify any operational inefficiencies in a typical business process. This early problem identification helps streamline corporate processes that align with a company’s business goals, thereby saving considerably on corporate spending.

Data mining plays a pivotal role in strategizing plans that help companies gain higher business profits and revenues and set them aside from their competitors.

See More: What Is Narrow Artificial Intelligence (AI)? Definition, Challenges, and Best Practices for 2022

Data Mining Techniques

Every data science application demands a different data mining technique. One of the popular and well-known data mining techniques used includes pattern recognition and anomaly detection. Both these methods employ a combination of techniques to mine data.

Let’s look at some of the fundamental data mining techniques commonly used across industry verticals.

1. Association rule

The association rule refers to the if-then statements that establish correlations and relationships between two or more data items. The correlations are evaluated using support and confidence metrics, wherein support determines the frequency of occurrence of data items within the dataset. In contrast, confidence relates to the accuracy of if-then statements.

For example, while tracking a customer’s behavior when purchasing online items, an observation is made that the customer generally buys cookies when purchasing a coffee pack. In such a case, the association rule establishes the relation between two items of cookies and coffee packs, thereby forecasting future buys whenever the customer adds the coffee pack to the shopping cart.

2. Classification

The classification data mining technique classifies data items within a dataset into different categories. For example, we can classify vehicles into different categories, such as sedan, hatchback, petrol, diesel, electric vehicle, etc., based on attributes such as the vehicle’s shape, wheel type, or even number of seats. When a new vehicle arrives, we can categorize it into various classes depending on the identified vehicle attributes. One can apply the same classification strategy to classify customers based on their age, address, purchase history, and social group.

Some of the examples of classification methods include decision trees, Naive Bayes classifiers, logistic regression, and so on.

3. Clustering

Clustering data mining techniques group data elements into clusters that share common characteristics. We can cluster data pieces into categories by simply identifying one or more attributes. Some of the well-known clustering techniques are k-means clustering, hierarchical clustering, and Gaussian mixture models.

4. Regression

Regression is a statistical modeling technique using previous observations to predict new data values. In other words, it is a method of determining relationships between data elements based on the predicted data values for a set of defined variables. This category’s classifier is called the ‘Continuous Value Classifier’. Linear regression, multivariate regression, and decision trees are key examples of this type.

5. Sequence & path analysis

One can also mine sequential data to determine patterns, wherein specific events or data values lead to other events in the future. This technique is applied for long-term data as sequential analysis is key to identifying trends or regular occurrences of certain events. For example, when a customer buys a grocery item, you can use a sequential pattern to suggest or add another item to the basket based on the customer’s purchase pattern.

6. Neural networks

Neural networks technically refer to algorithms that mimic the human brain and try to replicate its activity to accomplish a desired goal or task. These are used for several pattern recognition applications that typically involve deep learning techniques. Neural networks are a consequence of advanced machine learning research.

7. Prediction

The prediction data mining technique is typically used for predicting the occurrence of an event, such as the failure of machinery or a fault in an industrial component, a fraudulent event, or company profits crossing a certain threshold. Prediction techniques can help analyze trends, establish correlations, and do pattern matching when combined with other mining methods. Using such a mining technique, data miners can analyze past instances to forecast future events.

See More: What Is Computer Vision? Meaning, Examples, and Applications in 2022

Top 10 Data Mining Tools

Today, data mining is one of the crucial techs businesses need to flourish in this dynamic and volatile consumer-inclined market. It leverages BI and advanced analytics that give organizations a bird’s eye view of evolving market trends, which helps in better strategic planning and optimized decision-making.

According to an April 2021 report by ReportLinker, the global data mining tools market stood at $634.7 million in 2020 and is estimated to reach $1.3 billion by 2027.

Data mining benefits are facilitated through tools essential for anomaly detection in analytics models, trends, and patterns, thereby avoiding the possibility of a system getting compromised in the worst cases.

These are the top ten data mining tools in 2022:

1. RapidMiner

RapidMiner is a data mining platform that supports several algorithms essential for machine learning, deep learning, text mining, and predictive analytics. The tool provides a drag-and-drop facility on its interface along with pre-built models that help non-experts develop workflows without the need for explicit programming in specific scenarios such as fraud detection.

Subsequently, developers can leverage the benefits of R and Python to build analytic models that enable trend, pattern, and outlier visualization. Moreover, the tool is further supported by active community users that are always available for help.

Pricing: Free and open source data science platform, wherein the free plan analyzes 10k rows of data.

2. Oracle Data Mining

The Oracle Data Mining tool is a part of ‘Oracle Advanced Analytics’ that creates predictive models and comprises multiple algorithms essential for tasks such as classification, regression, prediction, and so on.

Oracle Data Mining allows businesses to identify and target prospective audiences, forecast potential customers, classify customer profiles, and even detect frauds as and when they occur. Moreover, the programmer community can integrate the analytics model into BI applications using a Java API to see complex trends and patterns.

Pricing: Oracle provides a 30-day free trial to potential buyers.

3. IBM SPSS Modeler

IBM SPSS Modeler is known to fasten the data mining process and visualize processed data better. The tool is suitable for non-programmer communities that can exercise the interface’s drag-and-drop functionality to build predictive models.

The tool enables the import of large volumes of data from several disparate sources to reveal hidden data patterns and trends. The basic version of the tool works with spreadsheets and relational databases, while text analytics features are available in the premium version.

Pricing: IBM offers a 30-day free trial.

4. Weka

Weka is an open-source ML tool written in JavaScript with a built-in framework for various ML algorithms. It has been developed by researchers at the University of Waikato in New Zealand. The tool offers an easy-to-use interface with additional features such as classification, regression, clustering, visualization, and much more. It allows users to build models crucial for testing ideas without writing code. This requires a good knowledge of the algorithms used for such purposes so that the appropriate one is rightly selected.

Weka tools were initially designed to explore the agricultural sector; however, today, it is being extensively used by researchers and scientists to explore the academic sector.

Pricing: The tool is available to download for free with a GNU General Public License.

5. KNIME

KNIME is built with machine learning capabilities and an intuitive interface that makes modeling to production much more accessible. The KNIME tool provides pre-built components that non-coders can access to develop analytical models without worrying about a single line of code.

KNIME supports integration features that make it a scalable platform that can process diverse data types and advanced algorithms. This tool is crucial for developing business intelligence and analytics applications. In finance, the tool finds use cases in credit scoring, fraud detection, and credit risk assessment.

Pricing: KNIME is free and an open-source data mining platform.

6. H2O

The H2O data mining tool brings AI technology into data science and analysis, making it accessible to every user. The tool is suitable for running several ML algorithms with features that support auto ML functions for the build and faster deployment of ML models.

H2O offers integration features through APIs available in standard programming languages and is suitable for managing complex datasets. The tool provides fully-managed options and the facility to deploy it in a hybrid setting.

Pricing: H2O is a free-to-use and open-source tool. However, enterprises can use an advanced version by paying for enterprise support and management.

7. Orange

Orange is a data science tool suitable for programming, testing, and visualizing data mining workflows. It is software that has built-in ML algorithms and text mining features, making it ideal for molecular scientists and biologists.

The tool provides an intuitive interface with add-on graphical features that make data visualization more interactive, such as sieve diagrams or silhouette plots. Moreover, the tool supports visual programming where non-experts in the domain can create models simply by using drag-and-drop interface features. At the same time, skilled professionals can rely on the Python programming language to develop models.

Pricing: Orange is a free and open-source platform.

8. Apache Mahout

Apache Mahout is a data mining tool that enables the creation of scalable applications using ML practices. The tool is an open-source platform designed for researchers and professionals who intend to implement their own algorithms.

Apache Mahout is built on a JavaScript foundation on top of the Apache Hadoop framework, known for recommender engines, clustering, and classification applications. The tool can handle large datasets and is preferred by companies such as LinkedIn and Yahoo.

Pricing: Free to use under the Apache license. Also, the tool finds excellent support from a larger user community.

9. SAS Enterprise Mining

SAS Enterprise Miner is a data mining platform that helps professionals better manage data by converting large chunks of data into valuable insights. The tool provides an intuitive interface that aids in faster analytical model building and supports various algorithms that help in data preparation, essential for advanced predictive models.

SAS Enterprise Mining is well-suited for companies intending to implement fraud detection applications or applications that enhance customer response rates targeted through marketing campaigns.

Pricing: Offers free software trial and customized pricing packages for advanced features.

10. Teradata

Teradata is a mining tool suitable for enterprises that rely on multi-cloud deployment setups. Such frameworks can easily access databases, data lakes, and even SaaS applications external to the enterprise. Moreover, with no-code deployment features, developing business models and analyzing them to make informed decisions becomes more manageable.

Teradata is open to deployment on any public cloud platform such as AWS, Google, and Azure. Data miners can also deploy the tool in on-premise settings or a private cloud.

Pricing: Teradata offers a flexible pricing model, which refrains from charging any upfront cost. It instead provides a pay-as-you-go model. Moreover, the tool offers a pricing calculator on the company’s website to help users determine the cost they incurred or may incur while they use the tool.

See More: What Is Logistic Regression? Equation, Assumptions, Types, and Best Practices

Takeaway

Data mining has opened up a sea of possibilities for companies by allowing them to improve and work on their bottom lines by identifying patterns and trends in business data. Mining techniques benefit every industry vertical, from retail, finance, manufacturing, insurance, and healthcare, to the entertainment and academic sectors.

With increased advancements and sophistication in technologies such as machine learning and artificial intelligence, data mining has become more automated, easy to use, and less expensive, making it suitable for smaller organizations and businesses.

Looking at the current technological progression, data mining can impact every field and application, from identifying the cheapest airfare to New York City to discovering new medical treatments for unknown diseases.

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