3 Key Takeaways on Data Mining

    1. The data mining process involves five stages: understanding your goals, understanding your data sources, preparing the data, data analysis, and results review.
    2. The technique that's right for you depends on your specific BI goals.
    3. A strong ETL platform is essential for effective data mining.

    Data mining techniques draw from a wide range of subjects, from database management to machine learning and everything in between. In this article, we will discuss the most important data mining techniques and how to employ them to maximize your data investments.

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    Table of Contents

    Data Mining Techniques in Action

    Businesses have access to more data than ever before. And all of this data can unearth patterns that are useful for business intelligence. The process of discovering these patterns in data is called data mining. Data mining techniques, when applied correctly, can drive business success. Before we get to the techniques, though, let's first understand the process of data mining.

    The data mining process involves five stages:

    Understanding the Goals of Your Data Mining Project 

    The first stage of data mining defines how the process will support your business goals. For example, what areas of business do you want to improve through data mining? Do you want to make your product recommendation systems better the way Netflix did? Do you want to understand your customers better through personas and segmentation?

    After codifying your data mining goals, you can develop a project timeline, key actions, and assign roles for completing the project. 

    Understanding Where Your Data Comes From

    Next, you need to assess your data sources. Data visualization tools like Google Data Studio, or Chartio allow you to explore the properties of your data to decide which information will be useful to achieve your goals. Understanding your data also helps you determine which data mining strategies will produce the insights you want.

    Preparing the Data with ETL

    Before data can be analyzed, it needs to be cleaned and organized. Data preparation happens through a process called ETL (extract, transform, load). You can use an automated ETL solution like Xplenty to extract your data from different business applications, SaaS platforms, and other sources—then transform the information and optimize it for high-speed analysis. Ultimately, the ETL process cleanses the data, addresses missing information, and makes sure your data mining applications can analyze the information as a whole. 

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    4. Analyzing, Mining, and Modeling the Data

    The prepared data is then fed into business intelligence (BI) tools—like Tableau Server, Looker, InsightSquared, Amazon QuickSight, or Microsoft Power BI. These tools use different machine learning algorithms for data mining to unearth patterns and forecast future trends.

    Related Reading: Top 17 Business Intelligence Tools of 2020

    5. Reviewing and Sharing the Findings Across the Organization

    The last stage of the data mining process is to review the results and answer key questions, such as:

    • If the findings are accurate
    • If they support your goals
    • How to act on them
    • How to share the findings with your team

    Most enterprise-level BI platforms allow you to distribute key findings from data mining across an organization efficiently.

    Essential Data Mining Techniques

    Techniques for data mining can encompass the entire gamut of data science, right from classification methods to complex machine learning algorithms. Here are some of the most widely used data mining techniques for business intelligence.

    Classification Analysis

    One of the most fundamental data mining techniques, it classifies data into different categories. The goal of classification analysis is to be able to predict behavior or answer a key business question. For example, take the case of a credit card company. The company is trying to determine which users in its database should get a credit card offer. By analyzing information such as purchase history and annual income, it can categorize users into 'low risk', 'medium risk', and 'high risk'.

    Another example of classification analysis is Gmail categorizing email as primary, social, or promotion, based on certain key attributes.

    Association Rule Learning

    This is a popular algorithm for market researchers. Association learning looks for interesting relationships between variables in massive datasets to reveal events that frequently occur together.

    For example, the system might discover that women aged 30 to 40 like to buy products with a specific shade of red. This would tell product designers to include that color in a new product line. Retailers can also use association analysis to find pairs of products that customers buy together. They can use the information for better purchase recommendations in an online marketplace.

    Regression Analysis

    Primarily used for forecasting, regression analysis is used for identifying the relationship between variables in a dataset. More specifically, it is used to predict continuous values based on other variables present in a dataset. For instance, you might use regression analysis to predict the future price of a product based on demand, availability, and other factors. There are different kinds of regression techniques. Two of the most common ones are:

    • Linear Regression: This algorithm predicts the value of an unknown variable by analyzing other variables. For example, you could train a linear regression model with data pertaining to recently-sold businesses (using data that includes business type, location, size, sale price, sale date, etc.). The linear regression model could then forecast the market value of another business based on location, sector, or future sale date. Here's a helpful guide to help you understand the differences between regression and correlation.

    The linear analysis could also reveal a trend of increasing monthly sales and forecast the trend into future months. Furthermore, it could zero-in on unique factors—like a new ad campaign or a change in packaging—to predict the effect of one or more factors on sales revenue.


    An illustration of random data points and their linear regression

    • Logistic Regressions: This algorithm is valuable for predicting whether a variable supports or does not support a specific result. For instance, logistic regression could analyze a dataset to answer the following yes-or-no questions:
    • a. Does the number of cigarettes you smoke per day influence your chances of getting lung cancer (yes or no)?
    • b. Does heart attack risk increase with age (yes or no)?

    For logistic regression to work, the variable needs to be “dichotomous.” In other words, you must be studying how the presence or non-presence of a variable affects a “yes-or-no” answer.


    This data mining technique organizes similar and dissimilar items together. Clustering identifies relationships between objects in an unstructured dataset to provide a meaningful, searchable, and analyzable structure. For example, if you use clustering to identify "look-alike" audiences in your dataset, you might learn that 25% of your customers are aged 45 to 50, female, and enjoy red wine. This information could prove valuable when targeting new customers in online advertising campaigns. 


    A representation of a k-means cluster analysis

    Outlier Detection

    Anomalies in data can provide actionable business intelligence. An anomaly, or an outlier, is a value or a set of values that deviate considerably from expected patterns. For example, if the majority of your sales happen on weekends, but you notice a substantial spike in purchases on weekdays in a particular week. Outlier detection as a data mining technique is particularly useful for fraud detection, intrusion monitoring, and performance monitoring of systems.

    Time Series Forecasting

    These machine learning models are used to predict the best timing for specific actions. They do so by using historical data and identifying patterns in historical data. For example, a vehicle manufacturer could analyze past data with a time series model to predict when it's necessary to restock inventories. Similarly, a retailer could use time forecasting to schedule the release of a new product. In this example, a data scientist used time-series forecasting to predict future demand for furniture and office supplies—including the best and worst months for selling such items.

    Decision Trees

    These are predictive modeling techniques that forecast outcomes based on a set of binary rules. By following the rules, a decision tree algorithm produces the same result with the same input. Decision trees are used for building classification models and regression analysis. There are various decision tree algorithms. Some of the most notable ones are:

    Here’s a simple decision tree for playing outside:


    Neural Networks

    Modeled after the human brain, neural networks learn through repetition over time. Neural network models are useful when machine learning systems require fast, rapid-fire responses. For instance, in driverless vehicle technology. Neural networks can be incredibly complex, though. Businesses might need to hire really skilled personnel to build and implement neural networks for data mining, effectively.


    A crucial part of data mining, visualization is a powerful tool to unearth data mining insights. Most modern data visualization tools use dashboards to quickly organize large datasets. Visualization as a data mining technique is also useful for finding incorrect information, combining variables that are highly correlated in order to reduce the dimensions of a dataset, and for variable selection. Some common data visualization methods are tree-maps, charts, heat maps, and histograms.

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    Sequential Pattern Mining

    Similar to the time series data mining technique, sequential pattern mining identifies events that happen in sequence. Mostly applied to transactional datasets, it can be useful for understanding customer behavior. For instance, you might use it to find a correlation between 'bag purchases' at your online store and previous purchases by customers. Sequential pattern data mining can inform product recommendations and up-sell opportunities.

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    Use Cases of Data Mining Techniques

    Modern organizations are using data mining to inform their business decisions in the following areas:

    Understanding Customer Satisfaction and Public Sentiment

    Companies are analyzing data from social media platforms through “text mining” to reveal how the public views their products and offerings. Text mining uses natural language processing (NLP) and statistical pattern recognition to understand overall feelings and sentiments based on what people are saying online. Once you understand the public sentiment, you can steer your marketing, PR, and product development to improve your reputation.

    Targeted Ads, Marketing, and Improved Recommendations

    Data mining is helping advertisers identify look-alike customers, so they can target them with tailored ads and promotions. Companies like Amazon and Netflix use these techniques to offer purchase recommendations based on customer browsing, viewing, and spending habits. Overall, it’s improving user engagement and experience, while boosting sales and retention.

    Medical Diagnosis and Patient Risk Assessment

    Data mining helps medical researchers improve patient diagnosis and treatment. The statistical models from data mining medical records have allowed doctors to create risk factor warnings and lifestyle recommendations for better preventative care.

    Insurance Industry Optimization

    Predictive analytics through data mining helps insurance companies understand their customers and the risks related to accidents, bodily injury, medical conditions, surgical outcomes, and property damage. Data mining also helps insurance companies identify the 1 out of 10 insurance claims that are fraudulent. By comparing one customer’s claim history to thousands, machine learning can find potential cases of fraud.

    Credit Risk Assessment

    Banks are mining data related to customer credit histories, credit scores, and demographics information—then applying machine learning algorithms to the information to automatically approve or deny loans and calculate more strategic interest rates.

    Financial Fraud and White-Collar Crime Prevention

    Financial institutions use data mining to red-flag potentially fraudulent transactions, which they pause while requesting customer verification by text or email. These machine learning models monitor customer spending habits to identify transactions that fall outside the norm.

    For example, MIT used machine learning to mine a dataset of 900 million transactions, where 122,000 were confirmed as fraudulent. Using insights from the data, MIT has improved fraud detection models for banks to dramatically reduce instances of financial fraud. 

    Xplenty: Fueling Your Data Mining

    Data mining can be incredibly powerful. It is being used by businesses around the world to improve user experiences and build better products. However, it can only be as powerful as the information it works on. Your data mining tools need to be supplied with clean, organized data that is ready for analysis. That's where Xplenty can help. Our automated, cloud-based ETL platform makes data integration a breeze. Schedule a demo and see for yourself how Xplenty is helping businesses get rid of data integration bottlenecks.