10 Data Mining Techniques to Drive Faster Business Insights

Modern organizations have access to massive volumes of data that need to be processed to unearth hidden insights. That’s where data mining can be helpful. It helps analysts utilize the power of data to its maximum potential, identify patterns and find anomalies, and explore ways to improve performance.

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What is Data Mining?

Data mining is the process of acquiring actionable insights from raw data. It makes it easier to analyze sizable amounts of data and identify hidden trends or patterns. The popularity of data mining grew from the increasing demands of enterprises to analyze data.

Here’s a quick guide on data mining to get you up to speed before we head-on.

10 Key Data Mining Techniques

Utilizing the right data mining techniques can maximize the use of raw data and minimize the challenges faced in extracting it. Consequently, data-driven business decisions call for smart data mining strategies. Below are the 10 most popular techniques data miners use to detect patterns in data to gather insights for informed decision-making.

1. Classification
2. Clustering
3. Prediction
4. Regression
5. Association
6. Outlier Detection
7. Sequential Patterns
8. Neural Networks
9. Data Warehousing
10. Machine Learning

1. Classification

This data mining technique comes in handy in applications where you want to classify data into categories based on certain attributes. It involves assigning values to new categories for data points based on the values in existing categories. Certain ‘classification rules’ define the relation of the derived categories with the existing categories. These rules are implemented on test data to check the validity of the results.

For instance, email providers such as Gmail may use classification to predict whether an email is a spam or not. If the algorithm predicts that the email is spam, it can be directed to the Spam folder. If the algorithm predicts that the email isn’t spam, it can be sent to the Inbox.

Caption: Data mining using classification

The above diagram shows that the classification algorithm uses some qualities of the mail to predict whether it’s spam or not. The set of rules, for example, can be:

• If the number of copies sent > 100 and the Subject contains ‘Online Gambling’ or ‘Lottery Winner’, Spam = Yes
• If Anonymity = Yes, Spam = Yes
• Default, Spam = No

2. Clustering

Clustering involves grouping the data based on their similarities. Unlike classification, where data points are analyzed with respect to their attributes, clustering uses data objects with no labelled attributes. By detecting similarities and differences between data objects, this technique can help create and populate attributes as its output.

The basic idea is to create clusters, such that the objects are similar to other objects in the same cluster but are different from objects in the other clusters. These similarities and dissimilarities can be used to detect useful features to help group unlabeled data.

A classic use case for this technique is customer profiling. For example, grouping customers with similar buying habits can help you generate targeted marketing campaigns for specific customer ‘clusters.’

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3. Prediction

Caption: Data mining using prediction

This data mining method uses historical and current data to make predictions for the future. You can think of the prediction technique as a combination of the existing data mining models, such as classification, trend analysis, clustering, etc.

A simple use case can be where a company wants to predict the revenue generated from an upcoming sale at their online store. The company can feed sales and profit data from past sales into a prediction algorithm. The predictive model traces patterns in the current and past data to generate a continuous-valued function that closely reflects the future profits.

4. Regression

Regression is a statistical modeling method that is used to understand the nature of the relationship among variables in a dataset. It can help you predict how the value of a dependent variable might change if one or more of the independent variables are changed.

For example, you may use the technique to project house values based on factors such as location, size, proximity to the city center, etc. A regression model can be developed using data collected for several houses. The data should include values for all the attributes that can contribute to the house’s value. The model can then be trained to predict home prices.

5. Association

Association, also called relation analysis, is used to detect patterns in data and discover correlations between items in a dataset. The most common application of the technique is ‘market basket analysis’ which predicts items that customers usually buy together.

The association model can tell you, for example, how likely a customer is to buy milk if they have added tea to the cart. Online stores often use this data mining strategy to make recommendations in the ‘customers also bought’ section on their pages.

6. Outlier Detection

Outlier detection deals with identifying items that don’t follow the characteristic behavior of the rest of the items in a dataset. Outlier mining, outer detection, anomaly detection, and anomaly analysis are all different names for the same technique. Data points that lie far off the expected pattern of the dataset are called outliers or anomalies.

Caption: Outlier Detection

The technique is often used in fraud detection, intrusion detection, and fault detection. In healthcare, outlier detection can be used to alert medical practitioners of a developing health condition if an unanticipated spike in a patient’s stats is observed.

7. Sequential Patterns

Sequential patterns is a data mining technique that lets you understand patterns observed in data over a period of time. For example, you may use it to see how the sales of a particular product in your catalog go up just before the holiday season, or with the start of summer.

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8. Neural Networks

Neural networks are a data mining technique that attempts to analyze data as a human brain would do. It uses a combination of processing units, similar to neurons in a human brain, to analyze and deduce relationships in data. As the model processes information, it learns, much like a human. It is employed in combination with AI and deep learning to create some of the strongest data mining models used today.

There is a range of applications where this technique can be used. For example, to predict customer behavior based on demographics so businesses can build targeted marketing campaigns. It’s also applicable in healthcare where these models can be used to find solutions for complex health conditions.

9. Data Warehousing

Data warehousing isn’t an independent data mining technique. Rather, it’s a useful process that helps prepare data for analysis and business intelligence. Businesses often have sets of data coming in from disparate sources. Before they can make any sense of this data, data miners need to collect and archive it in a data warehouse. This data can then be fed into a data mining model for analysis.

10. Machine Learning

Machine learning is one of the most complex forms of data mining. With machine learning, computers use algorithms and data to learn how to make decisions on their own. There are different types of machine learning models, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Supervised learning requires pre-labeled data to be fed to the algorithm to train the machine to classify it and predict outcomes.

Unsupervised learning, as the name suggests, handles unlabeled data, classifying it and identifying patterns on its own.

Semi-supervised learning uses a combination of the two models to train the machine.

Reinforcement learning involves training the machine using feedback from its experiences, with a ‘reward/punishment system for its actions.

Machine learning models can be used to make data-driven predictions in many industries. Fraud detection, customer recommendation systems, dynamic pricing, and real-time chatbots are just a few useful applications of machine learning.

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Challenges of Implementing Data Mining

With all its amazing benefits, there’s a catch. Implementing different types of data mining techniques in business processes comes with its challenges.

To name a few,

• You’ll need skilled experts to design data mining models in-house.
• Data mining often requires large databases that can be difficult and expensive to manage.
• Data mining often asks for noise-free data, requiring the control and handling of noise in data.
• Data from heterogeneous sources need to be unified in a single database and undergo data cleaning before it can be used.
• Complex data, such as audio, video, and images, can make it difficult to extract the required information.
• The results may not be accurate unless large datasets are used.
• The output, as insightful and accurate as it might be, may not directly be comprehensible to the end-user and may require the implementation of data visualization methods.

Data Mining Tools

With the overwhelming types of data mining techniques and challenges associated with their implementation, you need the right tools to create optimal results. You can create your own data mining software, but for that you will need big database systems and specialized data miners.

Alternatively, you can deploy an intuitive data mining tool that offers comprehensive features that almost anyone, with or without technical know-how, can use.